diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/README.md b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/README.md
new file mode 100644
index 00000000000..29854017660
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/README.md
@@ -0,0 +1,70 @@
+# Whisper Inference on CPU
+
+## LEGAL DISCLAIMER
+To the extent that any data, datasets, or models are referenced by Intel or accessed using tools or code on this site such data, datasets and models are provided by the third party indicated as the source of such content. Intel does not create the data, datasets, or models, provide a license to any third-party data, datasets, or models referenced, and does not warrant their accuracy or quality. By accessing such data, dataset(s) or model(s) you agree to the terms associated with that content and that your use complies with the applicable license. 
+
+Intel expressly disclaims the accuracy, adequacy, or completeness of any data, datasets or models, and is not liable for any errors, omissions, or defects in such content, or for any reliance thereon. Intel also expressly disclaims any warranty of non-infringement with respect to such data, dataset(s), or model(s). Intel is not liable for any liability or damages relating to your use of such data, datasets, or models. 
+
+## Launch the Docker Image
+Set the directories on the host system where model, dataset, and log files will reside. These locations will retain model and data content between Docker sessions.
+```
+export DATA_DIR="${DATA_DIR:-${PWD}/data}"
+export MODEL_DIR="${MODEL_DIR:-${PWD}/model}"
+export LOG_DIR="${LOG_DIR:-${PWD}/logs}"
+```
+
+## Launch the Docker Image
+In the Host OS environment, run the following after setting the proper Docker image name. If the Docker image is not on the system already, it will be retrieved from the registry.
+
+If retrieving the model or dataset, ensure any necessary proxy settings are run inside the container.
+```
+export DOCKER_IMAGE=intel/intel-optimized-pytorch:mlperf-inference-5.1-whisper
+
+docker run --privileged -it --rm \
+        --ipc=host --net=host --cap-add=ALL \
+        -e http_proxy=${http_proxy} \
+        -e https_proxy=${https_proxy} \
+        -v ${DATA_DIR}:/data \
+        -v ${MODEL_DIR}:/model \
+        -v ${LOG_DIR}:/logs \
+        --workdir /workspace \
+        ${DOCKER_IMAGE} /bin/bash
+```
+
+## Prepare workload resources [one-time operations]
+Download the model: Run this step inside the Docker container.  This operation will preserve the model on the host system using the volume mapping above.
+```
+bash scripts/download_model.sh
+```
+Download the dataset: Run this step inside the Docker container.  This operation will preserve the dataset on the host system using the volume mapping above.
+```
+bash scripts/download_dataset.sh
+```
+Calibrate the model: Run this step inside the Docker container.  This operation will create and preserve a calibrated model along with the original model file.
+```
+bash scripts/run_calibration.sh
+```
+
+## Run Benchmark
+Run this step inside the Docker container.  Select the appropriate scenario.  If this is the first time running this workload, the original model file will be calibrated to INT8 and stored alongside the original model file (one-time operation).  The default configuration supports Intel EMR.  If running GNR, please make the following [additional changes](GNR.md).
+
+Performance::
+```
+SCENARIO=Offline MODE=Performance bash run_mlperf.sh
+```
+Accuracy:
+```
+SCENARIO=Offline MODE=Accuracy    bash run_mlperf.sh
+```
+
+## Run Compliance Tests
+Run this step inside the Docker container.  After the benchmark scenarios have been run and results exist in {LOG_DIR}/results, run this step to complete compliance runs. Compliance output will be found in '{LOG_DIR}/compliance'.
+```
+SCENARIO=Offline MODE=Compliance  bash run_mlperf.sh
+```
+
+## Validate Submission Checker
+Run this step inside the Docker container.  The following script will perform accuracy log truncation and run the submission checker on the contents of {LOG_DIR}. The source scripts are distributed as MLPerf Inference reference tools. Ensure the submission content has been populated before running.  The script output is transient and destroyed after running.  The original content of ${LOG_DIR} is not modified.
+```
+VENDOR=Intel bash prepare_submission.sh
+```
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/accuracy.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/accuracy.py
new file mode 100755
index 00000000000..569d0c8473e
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/accuracy.py
@@ -0,0 +1,98 @@
+# Copyright 2025 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import argparse
+import array
+import json
+import sys
+import os
+from typing import List
+
+from whisper.normalizers import EnglishTextNormalizer
+
+from manifest import Manifest
+from legacy_helpers import __levenshtein, __gather_predictions
+from helpers import get_expanded_wordlist
+
+
+max_duration = float(os.environ.get("MAX_DURATION", "30.0"))
+labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
+dtype_map = {
+    "int8": 'b',
+    "int16": 'h',
+    "int32": 'l',
+    "int64": 'q',
+}
+
+def word_error_rate(hypotheses: List[str], references: List[str]) -> float:
+    """
+    Computes Average Word Error rate between two texts represented as
+    corresponding lists of string. Hypotheses and references must have same length.
+
+    Args:
+        hypotheses: list of hypotheses
+        references: list of references
+
+    Returns:
+        (float) average word error rate
+    """
+    normalizer = EnglishTextNormalizer()
+
+    scores = 0
+    words = 0
+    if len(hypotheses) != len(references):
+        raise ValueError("In word error rate calculation, hypotheses and reference"
+                         " lists must have the same number of elements. But I got:"
+                         "{0} and {1} correspondingly".format(len(hypotheses), len(references)))
+    for h, r in zip(hypotheses, references):
+        h = normalizer(h)
+        r = normalizer(r)
+        h_list = h.split()
+        r_list = r.split()
+        h_list = get_expanded_wordlist(h_list, r_list)
+        r_list = get_expanded_wordlist(r_list, h_list)
+        words += len(r_list)
+        scores += __levenshtein(h_list, r_list)
+    wer = scores / words
+    return wer, scores, words
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--log_dir", required=True)
+    parser.add_argument("--dataset_dir", required=True)
+    parser.add_argument("--manifest", required=True)
+    parser.add_argument("--output_dtype", default="int64", choices=dtype_map.keys(), help="Output data type")
+    args = parser.parse_args()
+    return args
+
+def main():
+    args = get_args()
+    manifest = Manifest(args.dataset_dir, [args.manifest], labels, len(labels), max_duration=max_duration)
+    with open(os.path.join(args.log_dir, "mlperf_log_accuracy.json")) as fh:
+        results = json.load(fh)
+    hypotheses = []
+    references = []
+    for result in results:
+        hypotheses.append(array.array(dtype_map[args.output_dtype], bytes.fromhex(result["data"])).tolist())
+        references.append(manifest[result["qsl_idx"]]["transcript"])
+
+    references = __gather_predictions([references], labels=labels)
+    hypotheses = __gather_predictions([hypotheses], labels=labels)
+
+    wer, _, _ = word_error_rate(hypotheses=hypotheses, references=references)
+    print("Word Error Rate: {:}%, accuracy={:}%".format(wer * 100, (1 - wer) * 100))
+
+if __name__ == '__main__':
+    main()
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/accuracy_eval.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/accuracy_eval.py
new file mode 100644
index 00000000000..4f252df2125
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/accuracy_eval.py
@@ -0,0 +1,98 @@
+# Copyright 2025 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import argparse
+import array
+import json
+import sys
+import os
+from typing import List
+
+from whisper.normalizers import EnglishTextNormalizer
+
+from manifest import Manifest
+from legacy_helpers import __levenshtein, __gather_predictions
+from helpers import get_expanded_wordlist
+
+
+max_duration = float(os.environ.get("MAX_DURATION", "30.0"))
+labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
+dtype_map = {
+    "int8": 'b',
+    "int16": 'h',
+    "int32": 'l',
+    "int64": 'q',
+}
+
+def word_error_rate(hypotheses: List[str], references: List[str]) -> float:
+    """
+    Computes Average Word Error rate between two texts represented as
+    corresponding lists of string. Hypotheses and references must have same length.
+
+    Args:
+        hypotheses: list of hypotheses
+        references: list of references
+
+    Returns:
+        (float) average word error rate
+    """
+    normalizer = EnglishTextNormalizer()
+
+    scores = 0
+    words = 0
+    if len(hypotheses) != len(references):
+        raise ValueError("In word error rate calculation, hypotheses and reference"
+                         " lists must have the same number of elements. But I got:"
+                         "{0} and {1} correspondingly".format(len(hypotheses), len(references)))
+    for h, r in zip(hypotheses, references):
+        h = normalizer(h)
+        r = normalizer(r)
+        h_list = h.split()
+        r_list = r.split()
+        h_list = get_expanded_wordlist(h_list, r_list)
+        r_list = get_expanded_wordlist(r_list, h_list)
+        words += len(r_list)
+        scores += __levenshtein(h_list, r_list)
+    wer = scores / words
+    return wer, scores, words
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--log_dir", required=True)
+    parser.add_argument("--dataset_dir", required=True)
+    parser.add_argument("--manifest", required=True)
+    parser.add_argument("--output_dtype", default="int64", choices=dtype_map.keys(), help="Output data type")
+    args = parser.parse_args()
+    return args
+
+def main():
+    args = get_args()
+    manifest = Manifest(args.dataset_dir, [args.manifest], labels, len(labels), max_duration=max_duration)
+    with open(os.path.join(args.log_dir, "mlperf_log_accuracy.json")) as fh:
+        results = json.load(fh)
+    hypotheses = []
+    references = []
+    for result in results:
+        hypotheses.append(array.array(dtype_map[args.output_dtype], bytes.fromhex(result["data"])).tolist())
+        references.append(manifest[result["qsl_idx"]]["transcript"])
+
+    references = __gather_predictions([references], labels=labels)
+    hypotheses = __gather_predictions([hypotheses], labels=labels)
+
+    wer, _, _ = word_error_rate(hypotheses=hypotheses, references=references)
+    print("Word Error Rate: {:}%, accuracy={:}%".format(wer * 100, (1 - wer) * 100))
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/calibration.md b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/calibration.md
new file mode 100644
index 00000000000..b028a6cda08
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/calibration.md
@@ -0,0 +1,37 @@
+## Intel MLPerf Inference Calibration and Quantization Details
+
+### RetinaNet Quantization
+Model Source: https://zenodo.org/record/6617981/files/resnext50_32x4d_fpn.pth
+
+Model Quantization: FP32 -> INT8
+
+Steps: /closed/Intel/code/retinanet/pytorch-cpu/scripts/run_calibration.sh
+
+### DLRMv2 Quantization
+Model Source: https://zenodo.org/record/5597155
+
+Model Quantization: FP32 -> INT8
+
+Steps: /closed/Intel/code/dlrm-v2-99.9/pytorch-cpu/scripts/run_calibration.sh
+
+### R-GAT Quantization
+Model Source: https://github.com/IllinoisGraphBenchmark/IGB-Datasets/
+
+Model Quantization: FP32 -> INT8
+
+Implementation: /closed/Intel/code/rgat/pytorch-cpu/backend.py
+
+### Whisper Quantization
+Model Source: https://huggingface.co/openai/whisper-large-v3
+
+Model Quantization: BF16 -> INT8
+
+Details: /closed/Intel/code/whisper/pytorch-cpu/scripts/run_calibration.sh
+
+### Llama3.1-8B Quantization
+Model Source: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
+
+Model Quantization: BF16 -> INT4
+
+Details: /closed/Intel/code/llama3.1-8b/pytorch-cpu/scripts/run_calibration.sh
+
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/common.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/common.txt
new file mode 100644
index 00000000000..7973da080c3
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/common.txt
@@ -0,0 +1,51 @@
+regex # Replace re for higher-performance regex matching
+cachetools
+psutil
+sentencepiece  # Required for LLaMA tokenizer.
+numpy
+requests >= 2.26.0
+tqdm
+blake3
+py-cpuinfo
+transformers >= 4.55.2
+tokenizers >= 0.21.1  # Required for fast incremental detokenization.
+protobuf # Required by LlamaTokenizer.
+fastapi[standard] >= 0.115.0 # Required by FastAPI's form models in the OpenAI API server's audio transcriptions endpoint.
+aiohttp
+openai >= 1.99.1  # For Responses API with reasoning content
+pydantic >= 2.11.7
+prometheus_client >= 0.18.0
+pillow  # Required for image processing
+prometheus-fastapi-instrumentator >= 7.0.0
+tiktoken >= 0.6.0  # Required for DBRX tokenizer
+lm-format-enforcer == 0.11.3
+llguidance >= 0.7.11, < 0.8.0; platform_machine == "x86_64" or platform_machine == "arm64" or platform_machine == "aarch64"
+outlines_core == 0.2.11
+# required for outlines backend disk cache
+diskcache == 5.6.3
+lark == 1.2.2
+xgrammar == 0.1.24; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64"
+typing_extensions >= 4.10
+filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
+partial-json-parser # used for parsing partial JSON outputs
+pyzmq >= 25.0.0
+msgspec
+gguf >= 0.13.0
+importlib_metadata; python_version < '3.10'
+mistral_common[image,audio] >= 1.8.2
+opencv-python-headless >= 4.11.0    # required for video IO
+pyyaml
+six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12
+setuptools>=77.0.3,<80; python_version > '3.11' # Setuptools is used by triton, we need to ensure a modern version is installed for 3.12+ so that it does not try to import distutils, which was removed in 3.12
+einops # Required for Qwen2-VL.
+compressed-tensors == 0.11.0 # required for compressed-tensors
+depyf==0.19.0 # required for profiling and debugging with compilation config
+cloudpickle # allows pickling lambda functions in model_executor/models/registry.py
+watchfiles # required for http server to monitor the updates of TLS files
+python-json-logger # Used by logging as per examples/others/logging_configuration.md
+scipy # Required for phi-4-multimodal-instruct
+ninja # Required for xgrammar, rocm, tpu, xpu
+pybase64 # fast base64 implementation
+cbor2 # Required for cross-language serialization of hashable objects
+setproctitle # Used to set process names for better debugging and monitoring
+openai-harmony >= 0.0.3  # Required for gpt-oss
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/configure_workload.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/configure_workload.sh
new file mode 100644
index 00000000000..5442e4284ed
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/configure_workload.sh
@@ -0,0 +1,160 @@
+#### THIS SCRIPT IS NOT INTENDED FOR INDEPENDENT RUN. IT CONTROLS RUN CONFIGURATION FOR run_mlperf.sh ####
+
+# Common workload parameters used by the run_mlperf.sh harness.
+export WORKLOAD="whisper"
+export MODEL="whisper"
+export IMPL="pytorch-xpu"
+export COMPLIANCE_TESTS="TEST01"
+export COMPLIANCE_SUITE_DIR=${WORKSPACE_DIR}/third_party/mlperf-inference/compliance/nvidia
+export MAX_LATENCY=10000000000
+
+# This function should handle each combination of the following parameters:
+# - SCENARIO: Offline or Server
+# - MODE: Performance, Accuracy, and Compliance
+workload_specific_run () {
+  export SCENARIO=${SCENARIO}
+  export MODE=${MODE}
+
+  # Standard ENV settings (potentially redundant)
+  export MODEL_DIR=${MODEL_DIR}/whisper-large-v3
+  export DATA_DIR=${DATA_DIR}
+  export MANIFEST_FILE=${DATA_DIR}/dev-all-repack.json
+  export USER_CONF=${USER_CONF}
+  export RUN_LOGS=${RUN_LOGS}
+
+
+  export VLLM_USE_V1=1
+  # export VLLM_LOGGING_LEVEL="DEBUG"
+  # export VLLM_ENABLE_V1_MULTIPROCESSING=0
+  # export VLLM_ATTENTION_BACKEND="FLASH_ATTN_VLLM_V1"
+  export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+  export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  export ONEAPI_DEVICE_SELECTOR=level_zero:0
+#  export EXTRA_ARGS="--accuracy"
+  if [ "${MODE}" == "Compliance" ]; then
+    export MODE="Performance"
+  fi
+
+  export EXTRA_ARGS=""
+  if [ "${MODE}" == "Accuracy" ]; then
+      export EXTRA_ARGS="--accuracy"
+  fi
+  python main.py \
+      --dataset_dir ${DATA_DIR} \
+      --model_path ${MODEL_DIR} \
+      --manifest ${MANIFEST_FILE} \
+      --scenario Offline \
+      --log_dir ${RUN_LOGS} \
+      --num_workers 1 \
+      ${EXTRA_ARGS}
+
+
+#  export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD
+#  export HF_HOME=${DATA_DIR}/huggingface
+#
+#  # Core count adaptation
+#  export NUM_NUMA_NODES=$(lscpu | grep "NUMA node(s)" | awk '{print $NF}')
+#  export NUM_CACHE_NODES=$(find /sys/devices/system/cpu/cpu*/cache -name level 2>/dev/null | xargs grep -l '^3$' 2>/dev/null | sed 's|/level|/shared_cpu_map|' | xargs -r cat 2>/dev/null | sort -u | wc -l)
+#  # With clustered L3 cache, use number of L3 caches instead of number of NUMA nodes
+#  if ((NUM_CACHE_NODES > NUM_NUMA_NODES)); then
+#    export NUM_NODES=NUM_CACHE_NODES
+#  else
+#    export NUM_NODES=NUM_NUMA_NODES
+#  fi
+#
+#  export NUM_CORES=$(($(lscpu | grep "Socket(s):" | awk '{print $2}') * $(lscpu | grep "Core(s) per socket:" | awk '{print $4}')))
+#  export CORES_PER_NODE=$(($NUM_CORES / $NUM_NODES))
+#
+#  # Golden config is 6 cores/inst, bs=96/inst
+#  # Allow degraded setup going from there
+#  MEM_AVAILABLE=$(free -g | awk '/Mem:/ {print $7}')
+#  MEM_PER_NODE=$(($MEM_AVAILABLE / $NUM_NODES))
+#  MEM_MODEL=4
+#  
+#  # Use BATCH_SIZE=96 if memory allows, 64, 48, 32, and lower if not able to fit
+#  # Aim for 4<=CORES_PER_INST<=10
+#  # Loop through the range [4, 10]
+#  MAX_CORES_PER_NODE=0
+#  for i in {4..10}; do
+#    # Check for divisibility
+#    MAX_INSTANCES_PER_NODE=$(( CORES_PER_NODE / i ))
+#    TEST_CORES_PER_NODE=$(( MAX_INSTANCES_PER_NODE * i ))
+#    if (( TEST_CORES_PER_NODE > MAX_CORES_PER_NODE )); then
+#      CORES_PER_INST_MIN=$i
+#      MAX_CORES_PER_NODE=${TEST_CORES_PER_NODE}
+#    fi
+#  done
+#
+#  # 1500 + 448 max tokens, 1280 d_model, 32 layers, k+v, 1 bit/value 8-bit
+#  CACHE_FACTOR_PER_BATCH=$((1948 * 32 * 1280 * 2))
+#  GIB_DIVISOR=1073741824
+#
+#  # Iterate through potential batch sizes from largest to smallest
+#  for BATCH_SIZE in 96 64 48 32; do
+#    MEM_CACHE=$((BATCH_SIZE * CACHE_FACTOR_PER_BATCH / GIB_DIVISOR))
+#    MEM_TOTAL=$((MEM_MODEL + MEM_CACHE))
+#
+#    # Ensure MEM_TOTAL and INSTS_PER_NODE are not zero to prevent division errors
+#    if (( MEM_TOTAL > 0 && (INSTS_PER_NODE = MEM_PER_NODE / MEM_TOTAL) > 0 )); then
+#      CORES_PER_INST=$((CORES_PER_NODE / INSTS_PER_NODE))
+#    else
+#      # Set to a high value if instance cannot run, to try the next smaller batch size
+#      CORES_PER_INST=999
+#    fi
+#
+#    # If cores per instance is acceptable, we found our batch size and can exit the loop
+#    if ((CORES_PER_INST <= 10)); then
+#      break
+#    fi
+#  done
+#
+#  if ((CORES_PER_INST < CORES_PER_INST_MIN)); then
+#    CORES_PER_INST=$CORES_PER_INST_MIN;
+#  fi
+#
+#  # Find the smallest CORES_PER_INST so that CORES_PER_NODE is divisible by CORES_PER_INST
+#  for ((i=CORES_PER_INST; i<=10; i++)); do
+#    if ((CORES_PER_NODE % i == 0)); then
+#      CORES_PER_INST=$i
+#      break
+#    fi
+#  done  
+#
+#  if [ "${SCENARIO}" == "Offline" ]; then
+#    export CORES_PER_INST=$CORES_PER_INST
+#    export VLLM_CPU_KVCACHE_SPACE=$MEM_CACHE
+#  fi
+#
+#  # Workload run-specific settings
+#  export MANIFEST_FILE="${DATA_DIR}/dev-all-repack.json"
+#
+#  if [ "${MODE}" == "Compliance" ]; then
+#    export MODE="Performance"
+#  fi
+#
+#  echo $CORES_PER_INST
+#  echo $VLLM_CPU_KVCACHE_SPACE
+#  
+#  # Using NUMA nodes here to not confuse SUT
+#  export INSTS_PER_NODE=$(($NUM_CORES / $NUM_NUMA_NODES / CORES_PER_INST))
+#  export NUM_INSTS=$((${INSTS_PER_NODE} * ${NUM_NUMA_NODES}))
+#
+#  export EXTRA_ARGS=""
+#  if [ "${MODE}" == "Accuracy" ]; then
+#      export EXTRA_ARGS="--accuracy"
+#  fi
+#
+#  cd ${WORKSPACE_DIR}
+#
+#  export MODEL_PATH=${MODEL_DIR}/whisper-large-v3-rtn
+#
+#  # Following v5.0 Inference release, one file handles all scenarios/modes: 
+#  python run.py \
+#      --dataset_dir ${DATA_DIR} \
+#      --model_path ${MODEL_PATH} \
+#      --manifest ${MANIFEST_FILE} \
+#      --scenario ${SCENARIO} \
+#      --log_dir ${RUN_LOGS} \
+#      --num_workers $NUM_INSTS \
+#      ${EXTRA_ARGS}
+}
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/Dockerfile b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/Dockerfile
new file mode 100644
index 00000000000..bcbd55e5b33
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/Dockerfile
@@ -0,0 +1,115 @@
+# Copyright (c) 2023 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+#
+# THIS IS A GENERATED DOCKERFILE.
+#
+# This file was assembled from multiple pieces, whose use is documented
+# throughout. Please refer to the TensorFlow dockerfiles documentation
+# for more information.
+
+ARG BASE_IMAGE=intel/deep-learning-essentials
+ARG BASE_TAG=2025.1.3-0-devel-ubuntu24.04
+FROM ${BASE_IMAGE}:${BASE_TAG} AS dev-base
+
+#Removing private keys from BASE
+RUN rm /etc/ssh/ssh_host_*_key && \
+    rm /etc/ssh/ssh_host_*_key.pub
+
+ARG http_proxy
+ARG https_proxy
+#ARG no_proxy
+
+ENV http_proxy=${http_proxy}
+ENV https_proxy=${https_proxy}
+#ENV no_proxy=${no_proxy}
+
+COPY ./ /workspace/
+WORKDIR /workspace
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/oneapi-archive-keyring.gpg > /dev/null && \
+    echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | tee /etc/apt/sources.list.d/oneAPI.list && \
+    add-apt-repository -y ppa:kobuk-team/intel-graphics-testing
+
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get autoclean && \
+    apt-get autoremove -y && \
+    apt-get install -y software-properties-common && \
+    add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get install -y --no-install-recommends \
+                    gcc \
+                    g++ \
+                    python3.12 \
+                    python3.12-venv \
+                    python3.12-dev \
+		    python3-pip \
+                    python-is-python3 \
+                    vim \
+                    wget \
+                    rsync \
+                    git \
+                    bc \
+                    unzip \
+                    zlib1g-dev \
+                    libgl1 # && \
+    # curl -skS https://bootstrap.pypa.io/get-pip.py | python3.10
+
+RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.12 1 && \
+    update-alternatives --install /usr/bin/python python /usr/bin/python3.12 1 
+
+# oneccl
+# TODO: Add script to download oneccl
+RUN cd /workspace && \
+    wget https://github.com/uxlfoundation/oneCCL/releases/download/2021.15.4/intel-oneccl-2021.15.4.11_offline.sh && \
+    bash /workspace/intel-oneccl-2021.15.4.11_offline.sh -a --silent --eula accept && echo "source /opt/intel/oneapi/setvars.sh --force" >> /root/.bashrc && \
+    rm -rf /workspace/intel-oneccl-2021.15.4.11_offline.sh
+
+RUN apt install -y --allow-downgrades \
+                   libze-intel-gpu1=25.18.33578.15-1146~24.04 \
+                   libze1=1.21.9.0-1136~24.04 \
+                   libze-dev=1.21.9.0-1136~24.04 \
+                   intel-opencl-icd=25.18.33578.15-1146~24.04 \
+                   intel-ocloc=25.18.33578.15-1146~24.04 \
+                   libigsc0 intel-gsc libmetee4 && \
+    wget https://github.com/intel/xpumanager/releases/download/V1.3.0/xpu-smi_1.3.0_20250707.103634.3db7de07.u24.04_amd64.deb && \
+    dpkg -i xpu-smi*.deb && \
+    rm -rf /var/lib/apt/lists/* && \
+    echo "alias ll='ls -l'" >> ~/.bashrc
+
+RUN python -m pip config set global.break-system-packages true && \
+    pip install torch==2.8.0 torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu && \
+    pip install packaging librosa openai-whisper ftfy
+
+# RUN pip install --no-deps accelerate==0.29.3 threadpoolctl==3.6.0 git+https://github.com/intel/auto-round.git@mlperf-awq
+
+# Bypass PEP 668
+RUN python -m pip config set global.break-system-packages true && \
+    cd /workspace/ && \
+    pip install --no-cache-dir -r requirements.txt && \
+    pip install --no-deps dist/*.whl # && rm -rf dist && bash prepare_loadgen.sh
+
+RUN mkdir -p /workspace/third_party && \
+    cd /workspace/third_party && \
+    git clone https://github.com/mlcommons/inference.git mlperf-inference && \
+    cd mlperf-inference && \
+    git checkout b9ed3c7 && \
+    cd loadgen && \
+    python3 -m pip install . && \
+    cp ../mlperf.conf /workspace/
+
+# Cleanup internal directories
+RUN for FILE in $(cat /workspace/redactions.txt); do rm -rf /workspace/${FILE}; done
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/build_container.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/build_container.sh
new file mode 100644
index 00000000000..1f74d875b07
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/build_container.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+
+export IMAGE_NAME="tiyengar:base_xpu"
+
+echo "Building XPU MLPerf workflow container"
+
+# Build the container
+docker build \
+    -f docker/Dockerfile \
+    --build-arg http_proxy=${http_proxy} \
+    --build-arg https_proxy=${https_proxy} \
+    -t ${IMAGE_NAME} .
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/run_container.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/run_container.sh
new file mode 100644
index 00000000000..b9c6ce1988a
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/docker/run_container.sh
@@ -0,0 +1,13 @@
+DOCKER_IMAGE=tiyengar:base_xpu
+# DOCKER_IMAGE=vllm_xpu:bkc_ww31
+docker run --privileged -it --rm \
+	-u root \
+        --ipc=host --net=host --cap-add=ALL \
+        --device /dev/dri:/dev/dri \
+        -v /dev/dri/by-path:/dev/dri/by-path \
+        -v /lib/modules:/lib/modules \
+        -v /data/dataset/librispeech:/data \
+        -v /data/model:/model \
+        -v ${PWD}/logs:/logs \
+	-v ${PWD}:/workspace \
+        ${DOCKER_IMAGE} /bin/bash
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/download_model.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/download_model.py
new file mode 100644
index 00000000000..8c5f0bde318
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/download_model.py
@@ -0,0 +1,14 @@
+#/bin/bash
+
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, AutoTokenizer
+
+model_id = "openai/whisper-large-v3"
+model_path = "/model/whisper-large-v3"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id)
+processor = AutoProcessor.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model.save_pretrained(model_path)
+processor.save_pretrained(model_path)
+tokenizer.save_pretrained(model_path)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/helpers.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/helpers.py
new file mode 100644
index 00000000000..a3799433e77
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/helpers.py
@@ -0,0 +1,79 @@
+# Copyright 2025 Intel Corporation. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from typing import List
+from legacy_helpers import __levenshtein
+
+def expand_concatenations(words_list: List, reference_dict: dict, reference_list: List):
+    """
+    Finds matching compound words in 'words_list' which exist as keys in 'reference_dict', if any.
+    If found, the compound word will be separated using reference_dict if the substitution reduces
+    the 'Levenshtein distance' between 'words_list' and 'reference_list'.
+    Args:
+        words_list: List of English word strings
+        reference_dict: Dictionary mapping compound words to a list a separated word strings.
+        reference_list: List of English word strings
+    Returns:
+        Modified 'word_string' with compound words replaced by individual strings, if any
+    """
+    score = __levenshtein(words_list, reference_list)
+
+    # Searches each word in 'word_list' for separability using the reference list. Once all options are
+    # considered, the modified 'word_list' is returned. Length of 'word_list' can grow, but not contract.
+    i = 0
+    words_length = len(words_list)
+    while i < words_length:
+        if words_list[i] in reference_dict.keys():
+            words_candidate = words_list[:i] + reference_dict[words_list[i]] + words_list[i + 1:]
+
+            # If levenshtein distance reduced, cache new word_list and resume search
+            candidate_levenshtein = __levenshtein(words_candidate, reference_list)
+            if candidate_levenshtein < score:
+                words_list = words_candidate
+                words_length = len(words_list)
+                score = candidate_levenshtein
+        i += 1
+    return words_list
+
+def get_expanded_wordlist(words_list: List, reference_list: List):
+    """
+    Provided two lists of English words, the two will be compared, and any compound words found in
+        'word_list' which are separated in 'reference_list' will be separated and the modified
+        'word_list' will be returned.
+    Args:
+        word_list: List of English word strings
+        reference_list: List of English word strings
+    Returns:
+        List of words modified from 'word_list' after expanding referenced compound words
+    """
+
+    # If levenshtein distance < 2, there cannot be any compound word separation issues.
+    if __levenshtein(words_list, reference_list) < 2:
+        return words_list
+
+    # Adding two-word compouding candidates to checklist
+    checklist = {}
+    for i in range(len(reference_list) - 1):
+        compound = reference_list[i] + reference_list[i + 1]
+        checklist[compound] = [reference_list[i], reference_list[i + 1]]
+
+    # Adding three-word compounding candidates to checklist
+    for i in range(len(reference_list) - 2):
+        compound = reference_list[i] + reference_list[i + 1] + reference_list[i + 2]
+        checklist[compound] = [reference_list[i], reference_list[i + 1], reference_list[i + 2]]
+
+    # All compiled candidates will be checked, and after checking for minimal Levenshtein
+    # distance, the modified list (or original if compounding not found) is directly returned 
+    return expand_concatenations(words_list, checklist, reference_list)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/internal/prepare_env.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/internal/prepare_env.sh
new file mode 100755
index 00000000000..550d7212c0a
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/internal/prepare_env.sh
@@ -0,0 +1,60 @@
+#!/bin/bash
+
+echo "Preparing wheels for XPU MLPerf workflow container"
+
+mkdir -p /workspace/dist
+
+# Clone internal repos
+mkdir -p third_party && pushd third_party
+# rm -rf ipex-gpu-internal vllm-internal
+
+# ipex
+git clone https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-gpu -b releases/2.8.10+xpu_rc ipex-gpu-internal
+# vllm
+git clone https://github.com/intel-sandbox/vllm-xpu # -b release/2509/vllm-xpu-0.10.1.1 vllm-internal
+popd
+
+# vllm
+pip uninstall -y vllm
+cd /workspace/third_party/vllm-internal
+git checkout 5774b0a1d
+pip install -r requirements/xpu.txt
+VLLM_TARGET_DEVICE=xpu python setup.py bdist_wheel
+cp dist/*.whl /workspace/dis
+cd ..
+# rm -rf vllm-internal
+
+# ipex internal
+export BUILD_WITH_CPU=OFF
+export BUILD_SEPARATE_OPS=ON
+export USE_AOT_DEVLIST='bmg'
+export TORCH_XPU_ARCH_LIST='bmg'
+# Use all threads if there is enough memory
+export MAX_JOBS=$(awk -v threads="$(nproc)" '/^Mem:/{mem=int($7/2); print(threads < mem ? threads : mem)}' <(free -g))
+
+cd /workspace/third_party/ipex-gpu-internal
+# Inside ipex-gpu source directory
+pip uninstall -y intel-extension-for-pytorch
+pip install -r requirements.txt
+git submodule sync
+git submodule update --init --recursive
+
+python setup.py bdist_wheel
+cp dist/*.whl /workspace/dist/
+pip install dist/*.whl
+cd ..
+# rm -rf ipex-gpu-internal
+
+pushd third_party
+git clone https://github.com/intel/auto-round.git
+cd autoround
+# git checkout mlperf-awq
+python setup.py install
+popd
+
+# Remove internal repos. Already built whls
+pushd third_party
+# rm -rf ipex-gpu-internal vllm-internal
+popd
+
+echo "Wheels prepared in dist/ directory"
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/legacy_helpers.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/legacy_helpers.py
new file mode 100644
index 00000000000..45065fff72e
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/legacy_helpers.py
@@ -0,0 +1,63 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2019, Myrtle Software Limited. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#           http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from enum import Enum
+from typing import List
+
+def __levenshtein(a: List, b: List) -> int:
+    """Calculates the Levenshtein distance between a and b.
+    """
+    n, m = len(a), len(b)
+    if n > m:
+        # Make sure n <= m, to use O(min(n,m)) space
+        a, b = b, a
+        n, m = m, n
+
+    current = list(range(n + 1))
+    for i in range(1, m + 1):
+        previous, current = current, [i] + [0] * n
+        for j in range(1, n + 1):
+            add, delete = previous[j] + 1, current[j - 1] + 1
+            change = previous[j - 1]
+            if a[j - 1] != b[i - 1]:
+                change = change + 1
+            current[j] = min(add, delete, change)
+
+    return current[n]
+
+def __whisper_decoder_predictions_tensor(tensor, labels):
+    """
+    Takes output of greedy whisper decoder and converts to strings.
+    Args:
+        tensor: model output tensor
+        label: A list of labels
+    Returns:
+        prediction
+    """
+    hypotheses = []
+    labels_map = dict([(i, labels[i]) for i in range(len(labels))])
+    # iterate over batch
+    for ind in range(len(tensor)):
+        hypothesis = ''.join([labels_map[c] for c in tensor[ind]])
+        hypotheses.append(hypothesis)
+    return hypotheses
+
+
+def __gather_predictions(predictions_list: list, labels: list) -> list:
+    results = []
+    for prediction in predictions_list:
+        results += __whisper_decoder_predictions_tensor(prediction, labels=labels)
+    return results
+
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/accuracy.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/accuracy.txt
new file mode 100644
index 00000000000..94fcd34f771
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/accuracy.txt
@@ -0,0 +1 @@
+Word Error Rate: 1.639344262295082%, accuracy=98.36065573770492%
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_accuracy.json b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_accuracy.json
new file mode 100644
index 00000000000..86c4ee7d473
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_accuracy.json
@@ -0,0 +1,3 @@
+[
+{ "seq_id" : 0, "qsl_idx" : 0, "data" : "17000000000000000500000000000000000000000000000008000000000000000100000000000000160000000000000005000000000000000000000000000000130000000000000015000000000000000300000000000000030000000000000005000000000000000500000000000000040000000000000005000000000000000400000000000000000000000000000009000000000000000E00000000000000000000000000000001000000000000000D0000000000000001000000000000001300000000000000130000000000000009000000000000000E000000000000000700000000000000000000000000000000000000000000000F0000000000000015000000000000000E0000000000000003000000000000000500000000000000130000000000000000000000000000000F0000000000000006000000000000000000000000000000130000000000000009000000000000000C00000000000000160000000000000005000000000000001200000000000000000000000000000005000000000000000E000000000000000F0000000000000015000000000000000700000000000000080000000000000000000000000000000900000000000000000000000000000014000000000000001200000000000000150000000000000013000000000000001400000000000000000000000000000014000000000000000F00000000000000000000000000000005000000000000001200000000000000050000000000000003000000000000001400000000000000000000000000000001000000000000000000000000000000080000000000000001000000000000000E00000000000000040000000000000013000000000000000F000000000000000D0000000000000005000000000000000000000000000000020000000000000012000000000000000F000000000000000E000000000000001A0000000000000005000000000000000000000000000000060000000000000009000000000000000700000000000000150000000000000012000000000000000500000000000000000000000000000014000000000000000F000000000000000000000000000000020000000000000005000000000000000000000000000000130000000000000015000000000000001200000000000000050000000000000000000000000000000900000000000000140000000000000000000000000000001300000000000000050000000000000005000000000000000D0000000000000013000000000000000000000000000000010000000000000000000000000000001300000000000000080000000000000001000000000000000D000000000000000500000000000000000000000000000019000000000000000500000000000000140000000000000000000000000000000900000000000000060000000000000000000000000000000900000000000000000000000000000003000000000000000F0000000000000015000000000000000C000000000000000400000000000000000000000000000013000000000000001400000000000000050000000000000001000000000000000C00000000000000000000000000000014000000000000000800000000000000050000000000000000000000000000000D000000000000000F000000000000000E0000000000000005000000000000001900000000000000000000000000000014000000000000000800000000000000090000000000000013000000000000000000000000000000100000000000000012000000000000000900000000000000050000000000000013000000000000001400000000000000000000000000000009000000000000001300000000000000000000000000000002000000000000000F0000000000000001000000000000001300000000000000140000000000000009000000000000000E0000000000000007000000000000000000000000000000010000000000000002000000000000000F000000000000001500000000000000140000000000000000000000000000000900000000000000000000000000000003000000000000000F0000000000000015000000000000000C00000000000000040000000000000000000000000000000C0000000000000009000000000000001600000000000000050000000000000000000000000000000100000000000000140000000000000000000000000000000500000000000000010000000000000013000000000000000500000000000000000000000000000006000000000000000F00000000000000120000000000000000000000000000001400000000000000080000000000000005000000000000000000000000000000120000000000000005000000000000001300000000000000140000000000000000000000000000000F00000000000000060000000000000000000000000000000D00000000000000190000000000000000000000000000000400000000000000010000000000000019000000000000001300000000000000000000000000000001000000000000000E000000000000000400000000000000000000000000000013000000000000000F00000000000000000000000000000008000000000000000500000000000000000000000000000002000000000000000500000000000000070000000000000001000000000000000E000000000000000000000000000000030000000000000001000000000000001300000000000000140000000000000009000000000000000E0000000000000007000000000000000000000000000000010000000000000002000000000000000F0000000000000015000000000000001400000000000000000000000000000008000000000000000F0000000000000017000000000000000000000000000000020000000000000005000000000000001300000000000000140000000000000000000000000000000800000000000000050000000000000000000000000000000D000000000000000900000000000000070000000000000008000000000000001400000000000000000000000000000003000000000000000F000000000000000D0000000000000010000000000000000100000000000000130000000000000013000000000000000000000000000000080000000000000009000000000000001300000000000000000000000000000010000000000000001500000000000000120000000000000010000000000000000F0000000000000013000000000000000500000000000000", "token_count" : 73 }
+]
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_detail.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_detail.txt
new file mode 100644
index 00000000000..fa14a69b2b7
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_detail.txt
@@ -0,0 +1,72 @@
+:::MLLOG {"key": "error_invalid_config", "value": "can't open file user.conf", "time_ms": 18446744073709.347379, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": true, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 606, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_version", "value": "5.1.0 @ b9ed3c7fec", "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 53, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_build_date_local", "value": "2025-10-27T20:20:14.738491", "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 55, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_build_date_utc", "value": "2025-10-27T20:20:14.738503", "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 56, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_git_commit_date", "value": "2025-07-25T18:23:04+01:00", "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 57, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_git_log_message", "value": "b9ed3c7fec1f16fe0576852bd3ade78100d86672 fix: update ds-r1 truncate max-output-len to 20k (was 32k) (#2290)\n6481ff4f8aab44514f37e0aa0270d66b7e0102ca Update README.md (#2288)\nd82d7d42ae9bfaff9941a6698954918504d8f0eb Fix ds-r1 acc checker output format not captured by submission checker (#2285)\na817ac5a374d42ae424da171b392eb1140a10d43 Fix missing comma: TEST01 not required for any LLMs. (#2283)\n4435cb582b10ce16ed6a68ff661bef06827f68eb Remove TEST01 for interactive scenario; add TEST06 for them (#2281)\n50de99161e33f32b569c7a00b6ccf56f274d418d Address issue that logger.info not captured by stdout; remove redundant logging (#2278)\n35d9836017ec2aea4b416f085980c81c1e90d682 Update documentation (#2279)\n9a1990e5d161144a1a3a44edb91211de78636bf6 Update download path for DeepSeek-R1 Dataset (#2275)\n7b9643c804dabb253e1fa2b811c700461ca9ed58 Fix SingleStream llama3.1-8b typo (#2274)\nfa32df9a9a4be1eab86774e260a217360a1ff64d Pinning vllm for speech-to-text reference (#2273)\nc57507b1227e1291a0535566d5988d0ab74ff376 Add interactive scenario in the TEST06, bump loadgen version to 5.1 (#2272)\n1446b3501c172153518b53871edbc1a0df014128 Update version generate_final_report.py (#2269)\n5232291860484b747ceeed7a327e56326e3eafe6 Update README.md (#2255)\n7d86e6b8b7564f99fef0c151fdeed7c67b53e392 Update download path for llama3.1_8b dataset (#2261)\nbcb600ed0301c23633906edeaa7f4367f2cc700c fix regex (#2260)\nbb0e01a3f47745ce7a5bd516c5064e6e7551076c accuracy (#2259)", "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 58, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_git_status_message", "value": "", "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 60, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "loadgen_file_sha1", "value": {"/.clang-format":"012aad77e5206c89d50718c46c119d1f3cb056b2","/CMakeLists.txt":"a8ebd64f62d0349aeedbe3295d833ebdce625c2e","/MANIFEST.in":"ddeb472d62edf2920db1f8fa3beebe3e831557f1","/README.md":"e850133bdbbfa62c84bc05a7358114d8996e0530","/README_BUILD.md":"5f6c6a784e9cd6995db47f9b9f70b1769909c9d8","/README_FAQ.md":"01f9ae9887f50bc030dc6107e740f40c43ca388f","/VERSION.txt":"204887433f1f70007f566f5bd6bbacbb68b15a6d","/__init__.py":"d013101621ef06a0ddc5e7d9ce511918a8b2ebe6","/bindings/c_api.cc":"14d178b64c7fc45d090e038c08d9b78ca943c383","/bindings/c_api.h":"23d9f99e00b2d196e095fae0bb453a391c18d601","/bindings/python_api.cc":"4dae966c92acdaa373b04a95adc4ca353937f154","/diagram_network_submission.png":"53dba8ad4272190ceb6335c12fd25e53dc02a8cb","/diagram_submission.png":"84c2f79309b237cef652aef6a187ba8e875a3952","/early_stopping.cc":"0cd7b546a389deac73f7955cd39255ed76557d62","/early_stopping.h":"158fcae6a5f47e82150d6416fa1f7bcef37e77fe","/issue_query_controller.cc":"02fcfe6d9cf958eeb4b6f1f4dbe87ba7eb4d7dec","/issue_query_controller.h":"ed20934fd3507a15949d501ac154be38e766f6ab","/loadgen.cc":"6daa9cd51454a699fcb55d9aa6bf9e54dd7b7a97","/loadgen.h":"ce9fcb5d44951e7e9048a83b7c1a41c8b8e0f7d8","/loadgen_integration_diagram.svg":"47f748307536f80cfc606947b440dd732afc2637","/logging.cc":"49e63158ebca654fa4b7c5f3321054cf4d6c3a30","/logging.h":"2102c91dedbaa156beadf0cecc63d2f43a2bd7dd","/mlperf.conf":"995a5e32f4e87da6ac0848cbdd8369e4ee4f321f","/mlperf_conf.h":"1cd5c9510eb0593e2721a3f3383e2e9d8a74d7ec","/pyproject.toml":"712fab87b72ba67ef2a068d0f9f47da65130342f","/query_dispatch_library.h":"1f18e9cd3ee4dc89a387cf462de1d0ceb1ece975","/query_sample.h":"c4f399103bc3d172079bbd4cd2b0ca0f22eebc4f","/query_sample_library.h":"8323a2225be1dff31f08ecc86b76eb3de06568bc","/requirements.txt":"a5ff7e77caa6e9e22ada90f0de0c865c987bf167","/results.cc":"fa04efe1049f62262eff7973d49cb2d90a406dcd","/results.h":"fce22d5a588d91fd968a6b25c27896dba87bc276","/setup.py":"a5eaa6f713bd3dfb6603be2c7928f0c295d7ee30","/system_under_test.h":"18d4809589dae33317d88d9beeb5491a6e1ccdec","/test_settings.h":"8e05582d1fbe9dd2b809686684c3a0ac41248723","/test_settings_internal.cc":"a5cc85fb7735727eee032aa3e88b5d61c1f11a2a","/test_settings_internal.h":"2bb9e9ae53904cb0ca221f4a5d49ca7d9ec3b0ca","/utils.cc":"3df8fdabf6eaea4697cf25d1dcb89cae88e36efd","/utils.h":"40775e32d619ea6356826ae5ea4174c7911f6894","/version.cc":"cbec2a5f98f9786c8c3d8b06b3d12df0b6550fa0","/version.h":"9d574baa64424e9c708fcfedd3dbb0b518a65fcc","/version_generator.py":"9f23d13276194588473120a8a6ecf5a6ed034a23"}, "time_ms": 0.005998, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "version.cc", "line_no": 67, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "test_datetime", "value": "2025-10-27T20:30:16Z", "time_ms": 0.006254, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1194, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "sut_name", "value": "PySUT", "time_ms": 0.006254, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1195, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "get_sut_name_duration_ns", "value": 284, "time_ms": 0.006254, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1196, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "qsl_name", "value": "PyQSL", "time_ms": 0.006254, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1197, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "qsl_reported_total_count", "value": 1, "time_ms": 0.006254, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1198, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "qsl_reported_performance_count", "value": 1, "time_ms": 0.006254, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "loadgen.cc", "line_no": 1199, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_scenario", "value": "Offline", "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 272, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_test_mode", "value": "AccuracyOnly", "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 273, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_offline_expected_qps", "value": 1, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 310, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_min_duration_ms", "value": 600000, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 316, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_max_duration_ms", "value": 0, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 317, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_min_query_count", "value": 1633, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 318, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_max_query_count", "value": 0, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 319, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_qsl_rng_seed", "value": 1780908523862526354, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 320, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_sample_index_rng_seed", "value": 14771362308971278857, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 321, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_schedule_rng_seed", "value": 18209322760996052031, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 323, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_accuracy_log_rng_seed", "value": 0, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 324, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_accuracy_log_probability", "value": 0, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 326, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_accuracy_log_sampling_target", "value": 0, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 328, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_print_timestamps", "value": false, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 330, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_performance_issue_unique", "value": false, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 331, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_performance_issue_same", "value": false, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 333, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_performance_issue_same_index", "value": 0, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 335, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_performance_sample_count_override", "value": 1633, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 337, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_sample_concatenate_permutation", "value": true, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 339, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "requested_use_token_latencies", "value": true, "time_ms": 0.011376, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 343, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_scenario", "value": "Offline", "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 418, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_test_mode", "value": "AccuracyOnly", "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 419, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_samples_per_query", "value": 1633, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 421, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_target_qps", "value": 1, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 422, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_target_latency_ns", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 423, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_target_latency_percentile", "value": 0.99, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 424, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_max_async_queries", "value": 1, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 426, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_target_duration_ms", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 427, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_min_duration_ms", "value": 600000, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 429, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_max_duration_ms", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 430, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_min_query_count", "value": 1, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 431, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_max_query_count", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 432, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_min_sample_count", "value": 1633, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 433, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_qsl_rng_seed", "value": 1780908523862526354, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 434, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_sample_index_rng_seed", "value": 14771362308971278857, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 435, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_schedule_rng_seed", "value": 18209322760996052031, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 437, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_accuracy_log_rng_seed", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 438, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_accuracy_log_probability", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 440, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_accuracy_log_sampling_target", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 442, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_print_timestamps", "value": false, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 444, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_performance_issue_unique", "value": false, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 445, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_performance_issue_same", "value": false, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 447, "pid": 1061, "tid": 1061}}
+:::MLLOG {"key": "effective_performance_issue_same_index", "value": 0, "time_ms": 0.012772, "namespace": "mlperf::logging", "event_type": "POINT_IN_TIME", "metadata": {"is_error": false, "is_warning": false, "file": "test_settings_internal.cc", "line_no": 449, "pid": 1061, "tid": 1061}}
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diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_summary.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_summary.txt
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+
+No warnings encountered during test.
+
+1 ERROR encountered. See detailed log.
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_trace.json b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/logs/mlperf_log_trace.json
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+{"name":"LogDetail","ph":"X","pid":1061,"tid":1750,"ts":5153929.763,"dur":0.813,"args":{"key":""power_end""}},
+{"name":"Thread","ph":"X","pid":1061,"tid":1750,"ts":5153914.067,"dur":21.364,"args":{"tid":1061}},
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+{"name":"Wait","ph":"X","pid":1061,"tid":1750,"ts":5153938.678,"dur":10053.822,"args":{}},
+{"name":"LastTrace"}
+],
+"displayTimeUnit":"ns",
+"otherData":{
+"ts":1761597016506290.500,
+"version":"MLPerf LoadGen v1.0"
+}
+}
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/main.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/main.py
new file mode 100644
index 00000000000..02970424159
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/main.py
@@ -0,0 +1,94 @@
+# Copyright 2020 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import os
+import argparse
+import subprocess
+from pathlib import Path
+
+import mlperf_loadgen as lg
+from sut import vllmSUT
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--scenario", choices=["SingleStream", "Offline", "Server"], default="Offline", help="Scenario")
+    parser.add_argument("--model_path", type=str, default="openai/whisper-large-v3")
+    parser.add_argument("--accuracy", action="store_true", help="enable accuracy pass")
+    parser.add_argument("--user_conf", default="user.conf", help="user config for user LoadGen settings such as target QPS")
+    parser.add_argument("--audit_conf", default="audit.config", help="audit config for LoadGen settings during compliance runs")
+    parser.add_argument("--dataset_dir", required=True)
+    parser.add_argument("--manifest", required=True)
+    parser.add_argument("--perf_count", type=int, default=None)
+    parser.add_argument("--log_dir", required=True)
+    parser.add_argument("--num_workers", default=1, type=int)
+    args = parser.parse_args()
+    return args
+
+
+scenario_map = {
+    "SingleStream": lg.TestScenario.SingleStream,
+    "Offline": lg.TestScenario.Offline,
+    "Server": lg.TestScenario.Server,
+}
+
+
+def main():
+    args = get_args()
+    print(args)
+
+    log_path = args.log_dir
+    os.makedirs(log_path, exist_ok=True)
+
+
+    sut = vllmSUT(dataset_dir=args.dataset_dir, 
+                  model_path=args.model_path,
+                  manifest_filepath=args.manifest, 
+                  perf_count=args.perf_count, 
+                  num_workers=args.num_workers)
+    sut.start()
+
+    settings = lg.TestSettings()
+    settings.scenario = scenario_map[args.scenario]
+    settings.FromConfig(args.user_conf, "whisper", args.scenario)
+
+    if args.accuracy:
+        settings.mode = lg.TestMode.AccuracyOnly
+    else:
+        settings.mode = lg.TestMode.PerformanceOnly
+
+    log_output_settings = lg.LogOutputSettings()
+    log_output_settings.outdir = log_path
+    log_output_settings.copy_summary_to_stdout = True
+    log_settings = lg.LogSettings()
+    log_settings.log_output = log_output_settings
+
+    print("Running Loadgen test...")
+    lg.StartTestWithLogSettings(sut.sut, 
+                                sut.qsl.qsl, 
+                                settings, 
+                                log_settings, 
+                                args.audit_conf)
+    sut.stop()
+
+    if args.accuracy:
+        acc_path = open(log_path + "/accuracy.txt", "w")
+        cmd = ["python3", "accuracy.py", "--log_dir", log_path, "--dataset_dir", args.dataset_dir, "--manifest", args.manifest]
+        subprocess.check_call(cmd, stdout=acc_path)
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/manifest.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/manifest.py
new file mode 100644
index 00000000000..8e1c195b0db
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/manifest.py
@@ -0,0 +1,127 @@
+# Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#           http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import string
+import os
+
+class Manifest(object):
+    def __init__(self, data_dir, manifest_paths, labels, blank_index, max_duration=None, pad_to_max=False,
+                 min_duration=None, sort_by_duration=False, max_utts=0,
+                 speed_perturbation=False, filter_speed=1.0):
+        self.labels_map = dict([(labels[i], i) for i in range(len(labels))])
+        self.blank_index = blank_index
+        self.max_duration = max_duration
+        ids = []
+        duration = 0.0
+        filtered_duration = 0.0
+
+        # If removing punctuation, make a list of punctuation to remove
+        table = None
+        for manifest_path in manifest_paths:
+            with open(manifest_path, "r", encoding="utf-8") as fh:
+                a = json.load(fh)
+                for data in a:
+                    files_and_speeds = data['files']
+
+                    if pad_to_max:
+                        if not speed_perturbation:
+                            min_speed = filter_speed
+                        else:
+                            min_speed = min(x['speed']
+                                            for x in files_and_speeds)
+                        max_duration = self.max_duration * min_speed
+
+                    data['duration'] = data['original_duration']
+                    if min_duration is not None and data['duration'] < min_duration:
+                        filtered_duration += data['duration']
+                        continue
+                    if max_duration is not None and data['duration'] > max_duration:
+                        filtered_duration += data['duration']
+                        continue
+
+                    # Prune and normalize according to transcript
+                    transcript_text = data[
+                        'transcript'] if "transcript" in data else self.load_transcript(
+                        data['text_filepath'])
+
+                    if not isinstance(transcript_text, str):
+                        print(
+                            "WARNING: Got transcript: {}. It is not a string. Dropping data point".format(
+                                transcript_text))
+                        filtered_duration += data['duration']
+                        continue
+                    data["transcript"] = self.parse_transcript(
+                        transcript_text)  # convert to vocab indices
+
+                    if speed_perturbation:
+                        audio_paths = [x['fname'] for x in files_and_speeds]
+                        data['audio_duration'] = [x['duration']
+                                                  for x in files_and_speeds]
+                    else:
+                        audio_paths = [
+                            x['fname'] for x in files_and_speeds if x['speed'] == filter_speed]
+                        data['audio_duration'] = [x['duration']
+                                                  for x in files_and_speeds if x['speed'] == filter_speed]
+                    data['audio_filepath'] = [os.path.join(
+                        data_dir, x) for x in audio_paths]
+                    data.pop('files')
+                    data.pop('original_duration')
+
+                    ids.append(data)
+                    duration += data['duration']
+
+                    if max_utts > 0 and len(ids) >= max_utts:
+                        print(
+                            'Stopping parsing %s as max_utts=%d' % (manifest_path, max_utts))
+                        break
+
+        if sort_by_duration:
+            ids = sorted(ids, key=lambda x: x['duration'])
+        self._data = ids
+        self._size = len(ids)
+        self._duration = duration
+        self._filtered_duration = filtered_duration
+
+    def load_transcript(self, transcript_path):
+        with open(transcript_path, 'r', encoding="utf-8") as transcript_file:
+            transcript = transcript_file.read().replace('\n', '')
+        return transcript
+
+    def parse_transcript(self, transcript):
+        chars = [self.labels_map.get(x, self.blank_index)
+                 for x in list(transcript)]
+        transcript = list(filter(lambda x: x != self.blank_index, chars))
+        return transcript
+
+    def __getitem__(self, item):
+        return self._data[item]
+
+    def __len__(self):
+        return self._size
+
+    def __iter__(self):
+        return iter(self._data)
+
+    @property
+    def duration(self):
+        return self._duration
+
+    @property
+    def filtered_duration(self):
+        return self._filtered_duration
+
+    @property
+    def data(self):
+        return list(self._data)
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/measurements.json b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/measurements.json
new file mode 100644
index 00000000000..9394cbc8847
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/measurements.json
@@ -0,0 +1,8 @@
+{
+    "starting_weights_filename": "https://huggingface.co/openai/whisper-large-v3/tree/main",
+    "weight_transformations": "Yes",
+    "weight_data_types": "INT8",
+    "input_data_types": "FP32",
+    "retraining": "No",
+    "weight_transformations": "Weight quantization from BF16 to INT8 using AutoRoundMLLM."
+}
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/mlperf.conf b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/mlperf.conf
new file mode 100644
index 00000000000..1b825514bdb
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/mlperf.conf
@@ -0,0 +1,164 @@
+# The format of this config file is 'key = value'.
+# The key has the format 'model.scenario.key'. Value is mostly int64_t.
+# Model maybe '*' as wildcard. In that case the value applies to all models.
+# All times are in milli seconds
+
+# Set performance_sample_count for each model.
+# User can optionally set this to higher values in user.conf.
+resnet50.*.performance_sample_count_override = 1024
+ssd-mobilenet.*.performance_sample_count_override = 256
+retinanet.*.performance_sample_count_override = 64
+bert.*.performance_sample_count_override = 10833
+dlrm.*.performance_sample_count_override = 204800
+dlrm-v2.*.performance_sample_count_override = 204800
+rnnt.*.performance_sample_count_override = 2513
+gptj.*.performance_sample_count_override = 13368
+mixtral-8x7b.*.performance_sample_count_override = 15000
+llama2-70b.*.performance_sample_count_override = 24576
+llama2-70b-interactive.*.performance_sample_count_override = 24576
+llama3_1-405b.*.performance_sample_count_override = 8313
+llama3_1-405b-interactive.*.performance_sample_count_override = 8313
+llama3_1-8b.*.performance_sample_count_override = 13368
+llama3_1-8b-edge.*.performance_sample_count_override = 5000
+llama3_1-8b-interactive.*.performance_sample_count_override = 13368
+stable-diffusion-xl.*.performance_sample_count_override = 5000
+rgat.*.performance_sample_count_override = 788379
+pointpainting.*.performance_sample_count_override = 1024
+deepseek-r1.*.performance_sample_count_override = 4388
+whisper.*.performance_sample_count_override = 1633
+# set to 0 to let entire sample set to be performance sample
+3d-unet.*.performance_sample_count_override = 0
+
+# Set seeds.
+*.*.qsl_rng_seed = 1780908523862526354
+*.*.sample_index_rng_seed = 14771362308971278857
+*.*.schedule_rng_seed = 18209322760996052031
+
+# Set seeds for TEST_05 (not needed from v5.0 onwards)
+*.*.test05_qsl_rng_seed = 7975553102935885558
+*.*.test05_sample_index_rng_seed = 11403566307062068064
+*.*.test05_schedule_rng_seed = 15816800565822761601
+
+*.SingleStream.target_latency_percentile = 90
+pointpainting.SingleStream.target_latency_percentile = 99.9
+*.SingleStream.min_duration = 600000
+
+*.MultiStream.target_latency_percentile = 99
+*.MultiStream.samples_per_query = 8
+*.MultiStream.min_duration = 600000
+*.MultiStream.min_query_count = 662
+retinanet.MultiStream.target_latency = 528
+
+# 3D-UNet uses equal issue mode because it has non-uniform inputs
+3d-unet.*.sample_concatenate_permutation = 1
+
+# R-GAT uses equal issue mode because it may have non-uniform inputs
+rgat.*.sample_concatenate_permutation = 1
+
+# LLM benchmarks have non-uniform inputs and outputs, and use equal issue mode for all latency scenario
+gptj.*.sample_concatenate_permutation = 1
+llama2-70b.*.sample_concatenate_permutation = 1
+llama2-70b-interactive.*.sample_concatenate_permutation = 1
+mixtral-8x7b.*.sample_concatenate_permutation = 1
+llama3_1-405b.*.sample_concatenate_permutation = 1
+llama3_1-405b-interactive.*.sample_concatenate_permutation = 1
+llama3_1-8b.*.sample_concatenate_permutation = 1
+llama3_1-8b-edge.*.sample_concatenate_permutation = 1
+llama3_1-8b-interactive.*.sample_concatenate_permutation = 1
+deepseek-r1.*.sample_concatenate_permutation = 1
+whisper.*.sample_concatenate_permutation = 1
+
+*.Server.target_latency = 10
+*.Server.target_latency_percentile = 99
+*.Server.target_duration = 0
+*.Server.min_duration = 600000
+resnet50.Server.target_latency = 15
+retinanet.Server.target_latency = 100
+bert.Server.target_latency = 130
+dlrm.Server.target_latency = 60
+dlrm-v2.Server.target_latency = 60
+rnnt.Server.target_latency = 1000
+gptj.Server.target_latency = 20000
+stable-diffusion-xl.Server.target_latency = 20000
+# Benchmarks that measure token latencies
+llama2-70b.*.use_token_latencies = 1
+llama2-70b-interactive.*.use_token_latencies = 1
+mixtral-8x7b.*.use_token_latencies = 1
+llama3_1-405b.*.use_token_latencies = 1
+llama3_1-405b-interactive.*.use_token_latencies = 1
+llama3_1-8b.*.use_token_latencies = 1
+llama3_1-8b-edge.*.use_token_latencies = 1
+llama3_1-8b-interactive.*.use_token_latencies = 1
+deepseek-r1.*.use_token_latencies = 1
+whisper.*.use_token_latencies = 1
+
+# gptj benchmark infers token latencies
+gptj.*.infer_token_latencies = 1
+gptj.*.token_latency_scaling_factor = 69
+# Only ttft and tpot are tracked for the llama2-70b, mixtral-8x7B & llama3_1-405b benchmark therefore target_latency = 0
+llama2-70b.Server.target_latency = 0
+llama2-70b.Server.ttft_latency = 2000
+llama2-70b.Server.tpot_latency = 200
+
+# Target Latencies for interactive setting
+llama2-70b-interactive.Server.target_latency = 0
+llama2-70b-interactive.Server.ttft_latency = 450
+llama2-70b-interactive.Server.tpot_latency = 40
+
+mixtral-8x7b.Server.target_latency = 0
+mixtral-8x7b.Server.ttft_latency = 2000
+mixtral-8x7b.Server.tpot_latency = 200
+
+llama3_1-405b.Server.target_latency = 0
+llama3_1-405b.Server.ttft_latency = 6000
+llama3_1-405b.Server.tpot_latency = 175
+
+# Target Latencies for interactive setting
+llama3_1-405b-interactive.Server.target_latency = 0
+llama3_1-405b-interactive.Server.ttft_latency = 4500
+llama3_1-405b-interactive.Server.tpot_latency = 80
+
+
+llama3_1-8b.Server.target_latency = 0
+llama3_1-8b.Server.ttft_latency = 2000
+llama3_1-8b.Server.tpot_latency = 100
+
+# Target Latencies for interactive setting
+llama3_1-8b-interactive.Server.target_latency = 0
+llama3_1-8b-interactive.Server.ttft_latency = 500
+llama3_1-8b-interactive.Server.tpot_latency = 30
+
+deepseek-r1.Server.target_latency = 0
+deepseek-r1.Server.ttft_latency = 2000
+deepseek-r1.Server.tpot_latency = 80
+
+*.Offline.target_latency_percentile = 90
+*.Offline.min_duration = 600000
+
+# In Offline scenario, we always have one query. But LoadGen maps this to
+# min_sample_count internally in Offline scenario. If the dataset size is larger
+# than 24576 we limit the min_query_count to 24576 and otherwise we use
+# the dataset size as the limit
+
+resnet50.Offline.min_query_count = 24576
+retinanet.Offline.min_query_count = 24576
+dlrm-v2.Offline.min_query_count = 24576
+bert.Offline.min_query_count = 10833
+gptj.Offline.min_query_count = 13368
+rnnt.Offline.min_query_count = 2513
+3d-unet.Offline.min_query_count = 43
+stable-diffusion-xl.Offline.min_query_count = 5000
+llama2-70b.Offline.min_query_count = 24576
+llama3_1-405b.Offline.min_query_count = 8313
+llama3_1-8b.Offline.min_query_count = 13368
+llama3_1-8b-edge.Offline.min_query_count = 5000
+mixtral-8x7b.Offline.min_query_count = 15000
+rgat.Offline.min_query_count = 788379
+deepseek-r1.Offline.min_query_count = 4388
+whisper.Offline.min_query_count = 1633
+
+# These fields should be defined and overridden by user.conf.
+*.SingleStream.target_latency = 10
+*.MultiStream.target_latency = 80
+*.Server.target_qps = 1.0
+*.Offline.target_qps = 1.0
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/prepare_loadgen.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/prepare_loadgen.sh
new file mode 100644
index 00000000000..f42f3c55250
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/prepare_loadgen.sh
@@ -0,0 +1,4 @@
+# MLPerf loadgen
+mkdir -p /workspace/third_party && cd /workspace/third_party && rm -rf mlperf-inference && \
+git clone https://github.com/mlcommons/inference.git mlperf-inference && cd mlperf-inference && git checkout b9ed3c7 && cd loadgen && \
+python3 -m pip install . && cp ../mlperf.conf ../../../ && cd ../../../
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/qsl.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/qsl.py
new file mode 100644
index 00000000000..19afd49acf9
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/qsl.py
@@ -0,0 +1,108 @@
+# Copyright 2025 The MLPerf Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+# Standard packages
+import sys
+import os
+from multiprocessing import Pool
+
+# Installed packages
+import numpy as np
+import librosa
+
+# Local python packages
+import mlperf_loadgen as lg
+from manifest import Manifest
+
+
+Manifest_Global = None
+max_duration = float(os.environ.get("MAX_DURATION", "30.0"))
+
+def load_sample_from_file(index):
+    global Manifest
+    sample = Manifest_Global[index]
+    filepath = sample['audio_filepath'][0]
+    prompt = {
+        "prompt": "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
+        "multi_modal_data": {
+            "audio": librosa.load(filepath, sr=16000),
+        },
+    }
+    duration = sample['duration']
+    return prompt
+
+class AudioQSL:
+    def __init__(self, dataset_dir, manifest_filepath, labels,
+                 sample_rate=16000, perf_count=None, skip_qsl=False):
+        global Manifest_Global
+        m_paths = [manifest_filepath]
+        self.manifest = Manifest(dataset_dir, m_paths, labels, len(labels), max_duration=max_duration)
+        Manifest_Global = self.manifest
+        self.sample_rate = sample_rate
+        self.count = len(self.manifest)
+        perf_count = self.count if perf_count is None else perf_count
+        self.sample_id_to_sample = {}
+        self.loaded = False
+        if skip_qsl:
+            self.qsl = None
+        else:
+            self.qsl = lg.ConstructQSL(self.count, perf_count,
+                                    self.load_query_samples,
+                                    self.unload_query_samples)
+
+        print(
+            "Dataset loaded with {0:.2f} hours. Filtered {1:.2f} hours. Number of samples: {2}".format(
+                self.manifest.duration / 3600,
+                self.manifest.filtered_duration / 3600,
+                self.count))
+    
+    def load_query_samples(self, sample_list):
+        pass
+
+    def unload_query_samples(self, sample_list):
+        pass
+
+    def __getitem__(self, index):
+        return self.sample_id_to_sample[index]
+
+    def __del__(self):
+        lg.DestroyQSL(self.qsl)
+        print("Finished destroying QSL.")
+
+# We have no problem fitting all data in memory, so we do that, in
+# order to speed up execution of the benchmark.
+class AudioQSLInMemory(AudioQSL):
+    def __init__(self, dataset_dir, manifest_filepath, labels,
+                 sample_rate=16000, perf_count=None, skip_qsl=True):
+        super().__init__(dataset_dir, manifest_filepath, labels,
+                         sample_rate, perf_count)
+        self.load_query_samples(range(self.count))
+
+    def load_query_samples(self, sample_list):
+        if not self.loaded:
+            pool = Pool(8)
+            print("pool size 8")
+            result = pool.map(load_sample_from_file, sample_list)
+            for sample_id in sample_list:
+                self.sample_id_to_sample[sample_id] = result[sample_id]
+            pool.close()
+            pool.join()
+            self.loaded = True
+
+    def unload_query_samples(self, sample_list):
+        for sample_id in sample_list:
+            del self.sample_id_to_sample[sample_id]
+    def __del__(self):
+        print("FInished destroying no QSL")
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize.py
new file mode 100644
index 00000000000..ab9a17d73d3
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize.py
@@ -0,0 +1,36 @@
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoProcessor, pipeline
+
+from auto_round import AutoRound
+
+model_path = "/model/whisper-large-v3"
+bits, group_size, sym, act_bits = 8, -1, True, 8
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_path, dtype=torch.float32, low_cpu_mem_usage=False, use_safetensors=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+processor = AutoProcessor.from_pretrained(model_path)
+
+# quantize the model
+'''
+autoround = AutoRound(model, tokenizer, processor,
+                        bits=bits, group_size=group_size, sym=sym, act_bits=act_bits,
+                        iters=0, 
+                        layer_config={
+                            "proj_out": {
+                                "bits": bits, 
+                                "group_size": group_size, 
+                                "sym": sym, 
+                                "act_bits": act_bits,
+                            }
+                        },
+                    )
+autoround.quantize()
+'''
+autoround = AutoRound(model, tokenizer, scheme="w4a16", iters=0, group_size=group_size, sym=sym, processor=processor)
+
+# save the quantized model, set format='auto_gptq' or 'auto_awq' to use other formats
+output_dir = f"/model/whisper-large-v3-w4a16" # w{bits}a{act_bits}g{group_size}"
+# autoround.save_quantized(output_dir, format="auto_awq", inplace=True)
+autoround.quantize_and_save(output_dir, format="auto_round", inplace=True)
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize_autoround.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize_autoround.py
new file mode 100644
index 00000000000..70f0d6710e9
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize_autoround.py
@@ -0,0 +1,105 @@
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoProcessor, pipeline
+
+from auto_round import AutoRoundMLLM, AutoRound
+
+model_path = "/models/whisper-large-v3"
+
+# W8A8 INT8
+bits, group_size, sym, act_bits = 8, -1, True, 8
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_path, torch_dtype=torch.float32, low_cpu_mem_usage=True, use_safetensors=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+processor = AutoProcessor.from_pretrained(model_path)
+
+## quantize the model
+autoround = AutoRoundMLLM(model, tokenizer, processor,
+                        bits=bits, group_size=group_size, sym=sym, act_bits=act_bits,
+                        iters=0,
+                        layer_config={
+                            "proj_out": {
+                                "bits": bits,
+                                "group_size": group_size,
+                                "sym": sym,
+                                "act_bits": act_bits,
+                            }
+                        },
+                    )
+autoround.quantize()
+
+# save the quantized model, set format='auto_gptq' or 'auto_awq' to use other formats
+output_dir = "./whisper-large-v3-iter200"
+autoround.save_quantized(output_dir, format='llm_compressor', inplace=True)
+
+exit()
+
+bits, group_size, sym, act_bits = 4, 32, True, 4
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_path, torch_dtype=torch.float32, low_cpu_mem_usage=True, use_safetensors=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+processor = AutoProcessor.from_pretrained(model_path)
+
+"""
+## quantize the model
+autoround = AutoRoundMLLM(model, tokenizer, processor,
+                        bits=bits, group_size=group_size, sym=sym, act_bits=act_bits,
+                        iters=0,
+                        layer_config={
+                            "proj_out": {
+                                "bits": bits,
+                                "group_size": group_size,
+                                "sym": sym,
+                                "act_bits": act_bits,
+                            }
+                        },
+                    )
+autoround.quantize()
+
+# save the quantized model, set format='auto_gptq' or 'auto_awq' to use other formats
+output_dir = "./whisper-large-v3-rtn-w4a16"
+autoround.save_quantized(output_dir, format='llm_compressor', inplace=True)
+"""
+
+
+"""
+autoround = AutoRoundMLLM(model, tokenizer, processor, scheme="W4A16", iters=0,
+        layer_config={
+            "proj_out": {
+                "bits": bits,
+                "group_size": group_size,
+                "sym": sym,
+                "act_bits": act_bits,
+            }
+        }
+)
+
+output_dir = "./whisper-large-v3-rtn-w4a16"
+
+
+# autoround.quantize()
+# autoround.save_quantized(output_dir, format='llm_compressor', inplace=True)
+# autoround.quantize_and_save(output_dir=output_dir, format="llm_compressor")
+autoround.quantize_and_save(output_dir=output_dir, format="auto_round")
+"""
+
+autoround = AutoRoundMLLM(model, tokenizer, processor, scheme="MXFP4", iters=0,
+        layer_config={
+            "proj_out": {
+                "bits": bits,
+                "group_size": group_size,
+                "sym": sym,
+                "act_bits": act_bits,
+            }
+        }
+)
+
+output_dir = "./whisper-large-v3-rtn-mxfp4"
+
+
+# autoround.quantize()
+# autoround.save_quantized(output_dir, format='llm_compressor', inplace=True)
+autoround.quantize_and_save(output_dir=output_dir, format="llm_compressor")
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize_compressored_tensors.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize_compressored_tensors.py
new file mode 100644
index 00000000000..9737f342ef1
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/quantize_compressored_tensors.py
@@ -0,0 +1,37 @@
+import torch
+from datasets import load_dataset
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.utils import dispatch_for_generation
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+# Select model and load it.
+# MODEL_ID = "openai/whisper-large-v3"
+MODEL_ID = "/models/whisper-large-v3"
+
+model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID, torch_dtype="auto")
+model.config.forced_decoder_ids = None
+processor = WhisperProcessor.from_pretrained(MODEL_ID)
+
+# Configure processor the dataset task.
+processor.tokenizer.set_prefix_tokens(language="en", task="transcribe")
+
+# recipe = QuantizationModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])
+recipe = QuantizationModifier(targets="Linear", scheme="W8A8", ignore=["lm_head"])
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+
+
+# that's where you have a lot of windows in the south no actually that's passive solar
+# and passive solar is something that was developed and designed in the 1960s and 70s
+# and it was a great thing for what it was at the time but it's not a passive house
+
+# Save to disk compressed.
+#SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-W4A16-G128"
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-W8A8-INT8"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/requirements.txt b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/requirements.txt
new file mode 100644
index 00000000000..d15fe9ad132
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/requirements.txt
@@ -0,0 +1,19 @@
+# Common dependencies
+-r common.txt
+
+ray>=2.9
+cmake>=3.26.1
+packaging>=24.2
+setuptools-scm>=8
+setuptools>=77.0.3,<80.0.0
+wheel
+jinja2>=3.1.6
+datasets # for benchmark scripts
+numba == 0.60.0 # v0.61 doesn't support Python 3.9. Required for N-gram speculative decoding
+nixl==0.3.0 # for PD disaggregation
+torch==2.8.0+xpu
+torchaudio
+torchvision
+--extra-index-url=https://download.pytorch.org/whl/xpu
+
+#intel-extension-for-pytorch @ https://intel-extension-for-pytorch.s3.us-east-1.amazonaws.com/ipex_dev/xpu/intel_extension_for_pytorch-2.8.10.post0%2Bxpu-cp312-cp312-linux_x86_64.whl
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run.sh
new file mode 100644
index 00000000000..a59fe2b6e61
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+export VLLM_USE_V1=1
+# export VLLM_LOGGING_LEVEL="DEBUG"
+# export VLLM_ENABLE_V1_MULTIPROCESSING=0
+# export VLLM_ATTENTION_BACKEND="FLASH_ATTN_VLLM_V1"
+export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export ONEAPI_DEVICE_SELECTOR=level_zero:0
+export EXTRA_ARGS="--accuracy"
+python main.py \
+      --dataset_dir /data \
+      --model_path /model/whisper-model.uri \
+      --manifest /data/whisper-dataset.uri/dev-all-repack.json \
+      --scenario Offline \
+      --log_dir ${PWD}/logs \
+      --num_workers 1 \
+      ${EXTRA_ARGS}
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run.sh.bak b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run.sh.bak
new file mode 100644
index 00000000000..73571fba6dc
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run.sh.bak
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+export VLLM_USE_V1=1
+# export VLLM_LOGGING_LEVEL="DEBUG"
+# export VLLM_ENABLE_V1_MULTIPROCESSING=0
+# export VLLM_ATTENTION_BACKEND="FLASH_ATTN_VLLM_V1"
+export VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
+export VLLM_WORKER_MULTIPROC_METHOD=spawn
+export ONEAPI_DEVICE_SELECTOR=level_zero:2
+export EXTRA_ARGS="--accuracy"
+python main.py \
+      --dataset_dir /data \
+      --model_path /model/whisper-large-v3 \
+      --manifest /data/dev-all-repack.json \
+      --scenario Offline \
+      --log_dir ${PWD}/logs \
+      --num_workers 1 \
+      ${EXTRA_ARGS}
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run_mlperf.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run_mlperf.sh
new file mode 100644
index 00000000000..1b0987a5f7a
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/run_mlperf.sh
@@ -0,0 +1,171 @@
+#!/bin/bash
+
+set -x
+
+# Controls workload mode
+export SCENARIO="${SCENARIO:-Offline}"
+export MODE="${MODE:-Performance}"
+export OFFLINE_QPS="${OFFLINE_QPS:-0}"
+export SERVER_QPS="${SERVER_QPS:-0}"
+export AUTO_USER_CONF="${AUTO_USER_CONF:-True}"
+export SYSTEM="${SYSTEM:-AUTO}"
+export DEBUG="${DEBUG:-False}"
+
+# Setting standard environmental paths
+export WORKSPACE_DIR=/workspace
+export DATA_DIR=/data
+export MODEL_DIR=/model
+export LOG_DIR=/logs
+export DOCUMENTATION_DIR=${LOG_DIR}/documentation
+export CODE_DIR=${LOG_DIR}/code
+export COMPLIANCE_DIR=${LOG_DIR}/compliance
+export MEASUREMENTS_DIR=${LOG_DIR}/measurements
+export RESULTS_DIR=${LOG_DIR}/results
+export SYSTEMS_DIR=${LOG_DIR}/systems
+
+##########     SUPPORT FUNCTIONS BEGIN HERE     ##########
+
+# Set HW specific qps settings from a select list of SKUs, or default.
+configure_system () {
+  export SYSTEM="1-node-1x-BMG-Pro-B60-Dual"
+#  export NUM_CORES=`lscpu -b -p=Core,Socket | grep -v '^#' | sort -u | wc -l`
+#  if [ "${SYSTEM}" != "AUTO" ] ; then
+#      export SYSTEM="${SYSTEM}"
+#  elif [ "${NUM_CORES}" == "256" ]; then
+#      export SYSTEM="1-node-2S-GNR_128C"
+#  elif [ "${NUM_CORES}" == "192" ]; then
+#      export SYSTEM="1-node-2S-GNR_96C"
+#  elif [ "${NUM_CORES}" == "172" ]; then
+#      export SYSTEM="1-node-2S-GNR_86C"
+#  else
+#      export SYSTEM="DEFAULT"
+#  fi
+
+  echo ${SYSTEM}
+}
+
+# Creates the default user.conf file, either auto-selected, modified, or newly generated.
+configure_userconf () {
+  # cd ${WORKSPACE_DIR}
+
+  # Ensure no left-over user.conf files from previous runs, and use pre-configured SYSTEM file if available.
+  if [ -f "${USER_CONF}" ]; then rm ${USER_CONF}; fi
+  if [ -f "systems/user.conf.${SYSTEM}" ]; then cp systems/user.conf.${SYSTEM} ${USER_CONF}; fi
+
+  # If an Offline QPS is manually specified, modify the existing user.conf or add to a new one.
+  if [ "${OFFLINE_QPS}" != "0" ]; then
+      if [ "$(grep "Offline.target_qps" user.conf | wc -l)" == "0" ]; then
+          echo "*.Offline.target_qps = ${OFFLINE_QPS}" >> ${USER_CONF}
+      else
+          sed -i 's/.*Offline.target_qps.*/\*\.Offline\.target_qps = '"${OFFLINE_QPS}"'/g' ${USER_CONF}
+      fi
+  fi
+
+  # If a Server QPS is manually specified, modify the existing user.conf or add to a new one.
+  if [ "${SERVER_QPS}" != "0" ]; then
+      if [ "$(grep "Server.target_qps" user.conf | wc -l)" == "0" ]; then
+          echo "*.Server.target_qps = ${SERVER_QPS}" >> ${USER_CONF}
+      else
+          sed -i 's/.*Server.target_qps.*/\*\.Server\.target_qps = '"${SERVER_QPS}"'/g' ${USER_CONF}
+      fi
+  fi
+}
+
+# Creates the non-run-specific submission files (necessary for final submission).
+prepare_suplements () {
+  # cd ${WORKSPACE_DIR}
+  # Ensure /logs/systems is populated or abort process.
+  if [ -f "systems/${SYSTEM}.json" ]; then
+    cp systems/${SYSTEM}.json ${SYSTEMS_DIR}/
+  else
+    echo '{ "submitter": "OEM", "system_name": "DEFAULT" }' > ${SYSTEMS_DIR}/${SYSTEM}.json
+  fi
+
+  # Populate /logs/code directory
+  cp -r README.md ${CODE_PATH}/
+
+  # Populate /logs/measurements directory
+  cp measurements.json ${MEASUREMENTS_PATH}/${SYSTEM}.json
+  cp README.md user.conf scripts/run_calibration.sh ${MEASUREMENTS_PATH}/
+
+  # Populate /logs/documentation directory
+  cp calibration.md ${DOCUMENTATION_DIR}/
+}
+
+# Initializes the system for an MLPerf run, then launches the run.
+run_workload () {
+  # cd ${WORKSPACE_DIR}
+  if [ "${DEBUG}" == "False" ] ; then bash run_clean.sh; fi
+  if [ -f "${RUN_LOGS}" ]; then rm -r ${RUN_LOGS}; fi
+  mkdir -p ${RUN_LOGS}
+  workload_specific_run
+}
+
+# Places the standard MLPerf run log outputs to the specified final dir.
+stage_logs () {
+  OUTPUT_PATH=$1
+  cd ${RUN_LOGS}
+  mkdir -p ${OUTPUT_PATH}
+  mv mlperf_log_accuracy.json mlperf_log_detail.txt mlperf_log_summary.txt ${OUTPUT_PATH}/
+  if [ -f accuracy.txt ]; then mv accuracy.txt ${OUTPUT_PATH}/; fi
+}
+
+##########     RUN BEGINS HERE     ##########
+
+# Using workload-specific parameters from 'configure_workload.sh', create the submission dir structure.
+source configure_workload.sh
+export SYSTEM="$(configure_system)"
+export CODE_PATH=${CODE_DIR}/${WORKLOAD}/${IMPL}
+export MEASUREMENTS_PATH=${MEASUREMENTS_DIR}/${SYSTEM}/${WORKLOAD}/${SCENARIO}
+export COMPLIANCE_PATH=${COMPLIANCE_DIR}/${SYSTEM}/${WORKLOAD}/${SCENARIO}
+export RESULTS_PATH=${RESULTS_DIR}/${SYSTEM}/${WORKLOAD}/${SCENARIO}
+mkdir -p ${SYSTEMS_DIR}
+mkdir -p ${CODE_PATH}
+mkdir -p ${MEASUREMENTS_PATH}
+mkdir -p ${DOCUMENTATION_DIR}
+
+# Ensuring the user.conf file is created if auto is enabled. If disabled, checks for existing one.
+export USER_CONF=user.conf
+if [ "${AUTO_USER_CONF}" == "True" ]; then configure_userconf; fi
+if [ -f "${USER_CONF}" ]; then
+  echo "LOG:::: Contents of user.conf:"
+  cat ${USER_CONF}
+else
+  echo "ERROR::: No user.conf file found."
+fi
+
+# Creates the non-runtime submission content (code, systems, measurements)
+if [ "${DEBUG}" == "False" ] ; then prepare_suplements; fi
+
+# Beginning workload runs, with Mode of: Performance, Accuracy, OR Compliance
+export RUN_LOGS=${WORKSPACE_DIR}/run_output
+if [ "${MODE}" == "Performance" ]; then
+    run_workload
+    stage_logs "${RESULTS_PATH}/performance/run_1"
+elif [ "${MODE}" == "Accuracy" ]; then
+    run_workload
+    stage_logs "${RESULTS_PATH}/accuracy"
+elif [ "${MODE}" == "Compliance" ]; then
+    for TEST in ${COMPLIANCE_TESTS}; do
+        echo "Running compliance ${TEST} ..."
+        
+	if [ -f ${WORKSPACE_DIR}/audit.config ]; then rm ${WORKSPACE_DIR}/audit.config; fi
+        if [ "$TEST" == "TEST01" ]; then
+            cp ${COMPLIANCE_SUITE_DIR}/${TEST}/${MODEL}/audit.config .
+        else
+            cp ${COMPLIANCE_SUITE_DIR}/${TEST}/audit.config .
+        fi
+
+	if ! [ -d ${RESULTS_PATH} ]; then
+	    echo "[ERROR] Compliance run could not be verified due to unspecified or non-existent RESULTS_PATH: ${RESULTS_PATH}"
+            exit
+	fi
+
+        OUTPUT_PATH=${RUN_LOGS}
+	run_workload
+
+        python ${COMPLIANCE_SUITE_DIR}/${TEST}/run_verification.py -r ${RESULTS_PATH} -c ${OUTPUT_PATH} -o ${COMPLIANCE_PATH}
+    done
+else
+    echo "[ERROR] Missing value for MODE. Options: Performance, Accuracy, Compliance"
+fi
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/download_dataset.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/download_dataset.sh
new file mode 100644
index 00000000000..062fc36c608
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/download_dataset.sh
@@ -0,0 +1,48 @@
+# Copyright (c) 2025 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+#!/bin/bash
+
+export WORKSPACE_DIR=/workspace
+export DATA_DIR=/data
+export LIBRISPEECH_DIR=${DATA_DIR}/LibriSpeech
+export UTILS_DIR=${WORKSPACE_DIR}/utils
+mkdir -p ${LIBRISPEECH_DIR}
+
+cd ${WORKSPACE_DIR}
+
+# Downloads all Librispeech dev partitions
+python ${UTILS_DIR}/download_librispeech.py \
+    ${UTILS_DIR}/inference_librispeech.csv \
+    ${LIBRISPEECH_DIR} \
+    -e ${DATA_DIR}
+
+# Consolidates all Librispeech partitions into common dir
+mkdir -p ${LIBRISPEECH_DIR}/dev-all
+cp -r ${LIBRISPEECH_DIR}/dev-clean/* \
+      ${LIBRISPEECH_DIR}/dev-other/* \
+      ${LIBRISPEECH_DIR}/dev-all/
+
+# Coverts original Librispeech flac to wav and creates manifest file
+python ${UTILS_DIR}/convert_librispeech.py \
+   --input_dir ${LIBRISPEECH_DIR}/dev-all \
+   --dest_dir ${DATA_DIR}/dev-all \
+   --output_json ${DATA_DIR}/dev-all.json
+
+# Repackages Librispeech samples into samples approaching 30s
+python utils/repackage_librispeech.py --manifest ${DATA_DIR}/dev-all.json \
+	                              --data_dir ${DATA_DIR} \
+				      --output_dir ${DATA_DIR}/dev-all-repack \
+				      --output_json /data/dev-all-repack.json
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/download_model.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/download_model.sh
new file mode 100644
index 00000000000..673220558eb
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/download_model.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python download_model.py
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/prepare_submission.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/prepare_submission.sh
new file mode 100644
index 00000000000..93d3630f2fe
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/prepare_submission.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+export LOG_DIR=/logs
+export SUBMISSION_DIR=${LOG_DIR}/submission-$(date +%s)
+export SUBMISSION_ORIGINAL=${SUBMISSION_DIR}/original
+export SUBMISSION_PROCESSED=${SUBMISSION_DIR}/processed
+
+RESULTS_DIR=${LOG_DIR}/results
+COMPLIANCE_DIR=${LOG_DIR}/compliance
+MEASUREMENTS_DIR=${LOG_DIR}/measurements
+SYSTEMS_DIR=${LOG_DIR}/systems
+echo "Ensuring correct system directories and files match system ${SYSTEM}."
+echo "The following are expected:"
+echo "- RESULTS: ${RESULTS_DIR}/${SYSTEM}"
+echo "- COMPLIANCE: ${COMPLIANCE_DIR}/${SYSTEM}"
+echo "- MEASUREMENTS: ${MEASUREMENTS_DIR}/${SYSTEM}"
+echo "- SYSTEM FILE: ${SYSTEMS_DIR}/${SYSTEM}.json"
+if ! [ -d ${RESULTS_DIR}/${SYSTEM} ];      then echo "[ERROR] RESULTS_DIR not found: ${RESULTS_DIR}/${SYSTEM}";           exit; fi
+if ! [ -d ${COMPLIANCE_DIR}/${SYSTEM} ];   then echo "[ERROR] COMPLIANCE_DIR not found: ${COMPLIANCE_DIR}/${SYSTEM}";     exit; fi
+if ! [ -d ${MEASUREMENTS_DIR}/${SYSTEM} ]; then echo "[ERROR] MEASUREMENTS_DIR not found: ${MEASUREMENTS_DIR}/${SYSTEM}"; exit; fi
+if ! [ -f ${SYSTEMS_DIR}/${SYSTEM}.json ]; then echo "[ERROR] SYSTEM file not found: ${SYSTEMS_DIR}/${SYSTEM}.json";      exit; fi
+
+echo "Verifying correct 'submitter' and 'system_name' fields in: ${SYSTEMS_DIR}/${SYSTEM}.json. These should match 'config/workload.conf'."
+if ! (( $(grep -r "\"submitter\": \"${VENDOR}\"" ${SYSTEMS_DIR}/${SYSTEM}.json | wc -l) > 0 ));   then echo "[ERROR] Field 'submitter' does not match 'VENDOR'.";   exit; fi
+if ! (( $(grep -r "\"system_name\": \"${SYSTEM}\"" ${SYSTEMS_DIR}/${SYSTEM}.json | wc -l) > 0 )); then echo "[ERROR] Field 'system_name' does not match 'SYSTEM'."; exit; fi
+
+mkdir -p ${SUBMISSION_ORIGINAL}/closed/${VENDOR}
+cp -r ${LOG_DIR}/code \
+      ${LOG_DIR}/compliance \
+      ${LOG_DIR}/documentation \
+      ${LOG_DIR}/measurements \
+      ${LOG_DIR}/results \
+      ${LOG_DIR}/systems \
+      ${SUBMISSION_ORIGINAL}/closed/${VENDOR}/
+
+echo "Truncating the logs: ${SUBMISSION_ORIGINAL} --> ${SUBMISSION_PROCESSED}"
+cd /workspace/third_party
+python mlperf-inference/tools/submission/truncate_accuracy_log.py --input ${SUBMISSION_ORIGINAL} --submitter ${VENDOR} --output ${SUBMISSION_PROCESSED}
+
+echo "Running submission checker: ${SUBMISSION_PROCESSED}"
+cd /workspace/third_party
+python3 mlperf-inference/tools/submission/submission_checker.py --input ${SUBMISSION_PROCESSED} --submitter=${VENDOR} --version=v5.1
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/run_calibration.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/run_calibration.sh
new file mode 100644
index 00000000000..5ffe28c161e
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/run_calibration.sh
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python quantize_model.py
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/run_full.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/run_full.sh
new file mode 100644
index 00000000000..0a9f2ca8f10
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/scripts/run_full.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+# Prepare workload resources [one-time operations]
+bash scripts/download_model.sh
+bash scripts/download_dataset.sh
+bash scripts/run_calibration.sh 
+
+# Run Benchmark (all scenarios)
+SCENARIO=Offline MODE=Performance bash run_mlperf.sh
+SCENARIO=Offline MODE=Accuracy    bash run_mlperf.sh
+
+# Run Compliance (all tests)
+SCENARIO=Offline MODE=Compliance  bash run_mlperf.sh
+
+# Build submission
+VENDOR=OEM SYSTEM=1-node-2S-GNR_128C bash scripts/prepare_submission.sh
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/sut.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/sut.py
new file mode 100644
index 00000000000..32ec86d5f49
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/sut.py
@@ -0,0 +1,440 @@
+# Copyright (c) 2020, Cerebras Systems, Inc. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#           http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Standard packages
+import sys
+import os
+import array
+import subprocess
+import math
+import queue
+import time
+import logging
+import threading
+
+# Common math packages
+import numpy as np
+from tqdm import tqdm
+
+# Framework packages
+import torch
+import torch.multiprocessing as mp
+from vllm import LLM, SamplingParams
+
+# Optimization packages
+
+# Local python packages
+from qsl import AudioQSL, AudioQSLInMemory
+import mlperf_loadgen as lg
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("SUT")
+
+def void(*args, **kwargs):
+    pass
+
+# Disable prints if progress bar is active
+PBAR = int(os.environ.get("PBAR", "1"))
+# Update frequency of the progress bar
+PBAR_FREQ = int(os.environ.get("PBAR_FREQ", "10"))
+if PBAR==1:
+    print = void
+
+SAMPLE_RATE = 16000
+
+labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
+labels_dict = {}
+for i in range(len(labels)):
+    labels_dict[labels[i]] = i
+
+class Instance(mp.Process):
+    def __init__(
+        self,
+        model_path=None,
+        dataset_path=None,
+        manifest_filepath=None,
+        device="cpu",
+        batch_size=-1,
+        total_sample_count=-1,
+        rank=-1,
+        input_queue=None,
+        output_queue=None,
+        cond_var=None,
+        alive_counter=None,
+        sample_counter=None,
+        current_counter=None,
+    ):
+        mp.Process.__init__(self)
+        self.model_path = model_path
+        self.dataset_path = dataset_path
+        self.manifest_filepath = manifest_filepath
+        self.device = device
+        self.batch_size = batch_size
+        self.total_sample_count = total_sample_count
+        self.rank = rank
+        self.input_queue = input_queue
+        self.output_queue = output_queue
+        self.cond_var = cond_var
+        self.alive_counter = alive_counter
+        self.sample_counter = sample_counter
+        self.num_samples = 0
+        self.total_time = 0
+        self.query_idx_mapping = []
+        self.qid_mapping = []
+        self.req_counter = 0
+        self.finished = False
+        self.current_counter = current_counter
+
+    def run(self):
+        dataset_vocab = labels
+
+        self.qsl = AudioQSLInMemory(
+            self.dataset_path,
+            self.manifest_filepath,
+            dataset_vocab,
+            SAMPLE_RATE,
+            self.total_sample_count
+        )
+
+        dtype="bfloat16"
+        print(f"Precision: {dtype}")
+        model = LLM(
+            model=self.model_path,
+            dtype=dtype,
+            skip_tokenizer_init=False,
+            trust_remote_code=True,
+            tensor_parallel_size=1,
+            max_num_seqs=64,
+            max_model_len=448,
+            max_num_batched_tokens=2000,
+            gpu_memory_utilization=0.95,
+            limit_mm_per_prompt={"audio": 1},
+            # kv_cache_dtype="fp8"
+        )
+        sampling_params = SamplingParams(
+            temperature=0,
+            top_p=1.0,
+            max_tokens=200,
+        )
+
+        self.model = model
+        self.sampling_params = sampling_params
+        with self.cond_var:
+            self.alive_counter.value += 1
+            self.cond_var.notify()
+
+        keep_alive = True
+        while keep_alive:
+            keep_alive = self.process_queries()
+
+    def process_queries(self):
+        samples_to_fill = self.batch_size - self.model.llm_engine.get_num_unfinished_requests()
+        # After receiving None, continue executing until all requests are finished
+        return_value = True
+        if (samples_to_fill>0 and not self.finished):
+            try:
+                qitem_list = self.input_queue.get(False)
+                print(f"Rank {self.rank} received one query")
+            except queue.Empty:
+                # When running multiple nodes/server, it's common for some workers to not receive a query in time
+                # Under that scenario, the work shouldn't wait (hence get(False)), and should also not treat
+                # the workload as finished
+                qitem_list = 1
+
+            if qitem_list is None:
+                self.finished = True
+            elif type(qitem_list)!=int:
+                # TODO: use time_start_list in server
+                qitem_list, time_start_list = qitem_list
+                prompt_list = []
+                for qitem in qitem_list:
+                    prompt = self.qsl[qitem.index]
+                    self.model.llm_engine.add_request(str(self.req_counter), prompt, self.sampling_params)
+                    self.query_idx_mapping.append(qitem.index)
+                    self.qid_mapping.append(qitem.id)
+                    self.req_counter += 1
+        results = []
+        query_ids = []
+        qid = []
+        if self.model.llm_engine.has_unfinished_requests():
+            print(f"Number of unfinished requests: {self.model.llm_engine.get_num_unfinished_requests()}")
+ 
+            # Step once
+            time_prestep = time.time()
+            step_outputs = self.model.llm_engine.step()
+            print(f"Step time {time.time()-time_prestep:.3f}s")
+            for output in step_outputs:
+                request_id = int(output.request_id)
+                # Process finished outputs
+                if output.finished:
+                    vllm_text = output.outputs[0].text
+                    token_ids = output.outputs[0].token_ids
+                    results.append((vllm_text, len(token_ids)))
+                    query_ids.append(self.query_idx_mapping[request_id])
+                    qid.append(self.qid_mapping[request_id])
+        elif self.finished:
+            return_value = False
+        else:
+            # Avoid excessive mpty steps
+            time.sleep(0.05)
+
+        self.num_samples += len(results)
+        
+        for i,result_tuple in enumerate(results):
+            # Whisper outputs space in the front and capitalizes things
+            result, n_tokens = result_tuple
+            result = result.lower().strip()
+            transcript = []
+            for s in result:
+                if s in labels_dict:
+                    transcript.append(labels_dict[s])
+            transcript = [transcript]
+
+            assert len(transcript) == 1
+            response_array = array.array('q', transcript[0])
+
+            self.output_queue.put((qid[i], n_tokens, response_array))
+            # print(f"Finished {qid[i]}")
+        with self.cond_var:
+            print(f"Rank {self.rank} finished {len(results)} requests. Running counter value {self.current_counter.value}")
+            self.current_counter.value -= len(results)
+            self.cond_var.notify()
+
+        return return_value
+
+class vllmSUT:
+    def __init__(self, dataset_dir, model_path,
+                 manifest_filepath, perf_count, num_workers=1):
+        self.model_path = model_path
+        self.dataset_path = dataset_dir
+        self.manifest_filepath = manifest_filepath
+        self.device = "cpu"
+        self.batch_size = 96
+        self.prefill_batch_size = 1
+        self.total_sample_count = perf_count
+        self.num_workers = num_workers
+        self.worker_threads = [None] * self.num_workers
+
+        dataset_vocab = labels
+
+        self.dev = torch.device("xpu:0") if torch.xpu.is_available() and os.environ.get("USE_GPU", "").lower() not in  [ "no", "false" ]  else torch.device("cpu")
+
+        self.sut = lg.ConstructSUT(self.issue_queries, self.flush_queries)
+        self.qsl = AudioQSLInMemory(dataset_dir,
+                                    manifest_filepath,
+                                    dataset_vocab,
+                                    SAMPLE_RATE,
+                                    perf_count)
+
+        self.query_queue_list = [mp.JoinableQueue() for _ in range(self.num_workers)]
+        self.query_queue_int = mp.Queue()
+        self.current_counter_list = [mp.Value("i", 0) for _ in range(self.num_workers)]
+        self.output_queue = mp.Queue()
+        self.alive_counter = mp.Value("i", 0)
+        self.cond_var = mp.Condition(lock=mp.Lock())
+        self.sample_counter = mp.Value("i", 0)
+        self.progress = None
+
+        # When using partial nodes, not all the workers will be active
+        # Load balancing should be between active workers
+        self.allowed_workers = []
+
+    def start(self):
+        expected_counter_value = 0 
+
+        for j in range(self.num_workers):
+            self.allowed_workers.append(j)
+            expected_counter_value += 1
+
+            worker = Instance(
+                model_path=self.model_path,
+                dataset_path=self.dataset_path,
+                manifest_filepath=self.manifest_filepath,
+                device=self.device,
+                batch_size=self.batch_size,
+                total_sample_count=self.total_sample_count,
+                rank=j,
+                input_queue = self.query_queue_list[j],
+                output_queue = self.output_queue,
+                cond_var = self.cond_var,
+                alive_counter = self.alive_counter,
+                sample_counter = self.sample_counter,
+                current_counter = self.current_counter_list[j]
+            )
+            worker.start()
+            self.worker_threads[j] = worker
+
+        with self.cond_var:
+            self.cond_var.wait_for(lambda: self.alive_counter.value == expected_counter_value)
+
+        log.info(f"Starting internal issue query thread")
+        self.query_thread = threading.Thread(target=self.issue_queries_int)
+        self.query_thread.daemon = True
+        self.query_thread.start()
+
+        log.info(f"Starting Loadgen response thread")
+        response_thread = threading.Thread(target=self.response_loadgen)
+        response_thread.daemon = True
+        response_thread.start()
+
+    def get_best_rank(self, value_added):
+        current_counters = np.array([(self.current_counter_list[i].value+value_added) for i in self.allowed_workers]) # Instances priority will be ordered by their respective in-flight queries
+        target_rank = np.argmin(current_counters)
+        if current_counters[target_rank]>self.batch_size:
+            return -1
+        else:
+            return self.allowed_workers[target_rank]
+
+    def issue_queries(self, query_samples):
+        start_time = time.time()
+        if PBAR and (self.progress is None):
+            if len(query_samples)>1:
+                self.progress = tqdm(total=len(query_samples), smoothing=0.0)
+            else:
+                self.progress = tqdm(smoothing=0.0)
+
+        query_sample_list = []
+        for query_sample in query_samples:
+            # Continuous batching
+            self.query_queue_int.put((query_sample, start_time))
+
+    def try_dispatch(
+        self,
+        query_list,
+        delete_list,
+        bucket,
+        input_len_bucket,
+        time_start_bucket,
+        server=False): # Inactive for now
+        target_rank = -1
+        wait_to_dispatch = True
+        while wait_to_dispatch:
+            with self.cond_var:
+                target_rank = self.get_best_rank(1) # With prepacking, prefill batch size is always 1
+                if target_rank!=-1:
+                    self.current_counter_list[target_rank].value += 1
+            # Always wait for an instance with an empty slot(for now)
+            if target_rank==-1:
+                time.sleep(0.01)
+            else:
+                wait_to_dispatch = False
+        if target_rank!=-1:
+            print(f"Sending to rank {target_rank}, num_queries {len(query_list)}, before add {self.current_counter_list[target_rank].value}")
+            self.query_queue_list[target_rank].put((query_list, time_start_bucket))
+            delete_list.append(bucket)
+            return True
+        return False
+
+    # Generic load balancer. Watered-down version of bucketed prefill
+    # Since there is not bucket, there is no point to use bucketed query list
+    def issue_queries_int(self):
+        keep_alive = True
+        # TODO: add server and use real latency
+        time_left = 9999
+        time_compute = 0
+        time_start_list = []
+        query_list = []
+
+        while keep_alive:
+            print("Length of query_list", len(query_list))
+            new_query = False
+            try:
+                query = self.query_queue_int.get(timeout=0.05)
+            except:
+                pass
+            else:
+                if query is None:
+                    keep_alive = False
+                else:
+                    query, start_time = query
+                    start_time_c = start_time
+                    new_query = True
+            
+            if new_query:
+                time_start_list.append(start_time_c)
+                query_list.append(query)
+            
+            if len(query_list)>0:
+                time_wait = time.time()-time_start_list[0]
+                time_needed = time_wait + time_compute
+                # When to dispatch
+                # 1. Time limit passed
+                # 2. Reached prefill batch size
+                # 3. End of inference
+                if (time_needed > time_left) or (len(query_list)==self.prefill_batch_size) or (not keep_alive):
+                    target_rank = -1
+                    # Continue trying to dispatch until there is an empty spot
+                    while target_rank==-1:
+                        with self.cond_var:
+                            target_rank = self.get_best_rank(len(query_list))
+                            if target_rank!=-1:
+                                self.current_counter_list[target_rank].value += len(query_list)
+                        if target_rank==-1:
+                            time.sleep(0.01)
+                    print(f"Sending to rank {target_rank}, length {len(query_list)}, wait_time {time_wait:.2f}s")
+                    self.query_queue_list[target_rank].put((query_list, time_start_list))
+                    query_list = []
+                    time_start_list = []
+        
+        for i in range(self.num_workers):
+            print("Putting none in", i)
+            self.query_queue_list[i].put(None)
+
+    def flush_queries(self):
+        self.query_queue_int.put(None)
+        pass
+
+    def update_pbar(self, tok_count, last_count, update_value):
+        postfix_str = f"{tok_count/self.progress.format_dict['elapsed']:.1f}toks/s"
+        self.progress.set_postfix_str(postfix_str, refresh=False)
+        self.progress.update(update_value)
+        self.progress.refresh()
+        last_count += update_value
+        return last_count
+
+    def response_loadgen(self):
+        keep_alive = True
+        processed_count = 0
+        last_count = 0
+        tok_count = 0
+        while keep_alive:
+            result = self.output_queue.get()
+            if result is None:
+                keep_alive = False
+            else:
+                qid, n_tokens, response_array = result
+                bi = response_array.buffer_info()
+                response = lg.QuerySampleResponse(qid, bi[0],
+                                                  bi[1] * response_array.itemsize, n_tokens)
+                lg.QuerySamplesComplete([response])
+                processed_count += 1
+                tok_count += n_tokens
+                if PBAR:
+                    if processed_count - last_count >= PBAR_FREQ:
+                        last_count = self.update_pbar(tok_count, last_count, PBAR_FREQ)
+        if PBAR and processed_count>last_count:
+            last_count = self.update_pbar(tok_count, last_count, processed_count-last_count)
+
+    def stop(self):
+        self.output_queue.put(None)
+        for worker in self.worker_threads:
+            try:
+                worker.kill()
+            except:
+                pass
+
+
+    def __del__(self):
+        lg.DestroySUT(self.sut)
+        print("Finished destroying SUT.")
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/systems/1-node-1x-BMG-Pro-B60-Dual.json b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/systems/1-node-1x-BMG-Pro-B60-Dual.json
new file mode 100644
index 00000000000..91ceef6921d
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/systems/1-node-1x-BMG-Pro-B60-Dual.json
@@ -0,0 +1,38 @@
+{
+    "division": "closed",
+    "submitter": "Intel",
+    "status": "available",
+    "system_type":"datacenter",
+    "system_type_detail":"N/A",
+    "system_name": "1-node-2S-GNR_128C",
+    "number_of_nodes": "1",
+    "host_processor_model_name": "INTEL(R) XEON(R) 6980P",
+    "host_processors_per_node": "2",
+    "host_processor_core_count": "128",
+    "host_processor_frequency": "N/A",
+    "host_processor_caches": "N/A",
+    "host_memory_configuration": "24 slots per socket / 96GB each / 8800 MT/s DDR5 (MRDIMM)",
+    "host_memory_capacity": "2304GB",
+    "host_storage_capacity": "N/A",
+    "host_storage_type": "N/A",
+    "host_processor_interconnect": "N/A",
+    "host_networking": "I210 Gigabit Network Connection",
+    "host_networking_topology": "N/A",
+    "host_network_card_count": "1",
+    "accelerators_per_node": "0",
+    "accelerator_model_name": "N/A",
+    "accelerator_frequency": "N/A",
+    "accelerator_host_interconnect": "N/A",
+    "accelerator_interconnect": "N/A",
+    "accelerator_interconnect_topology": "N/A",
+    "accelerator_memory_capacity": "N/A",
+    "accelerator_memory_configuration": "N/A",
+    "accelerator_on-chip_memories": "N/A",
+    "cooling": "Air",
+    "hw_notes": "N/A",
+
+    "framework": "PyTorch",
+    "operating_system": "CentOS Stream 9",
+    "other_software_stack": "6.12.0",
+    "sw_notes": "N/A"
+}
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/systems/user.conf.1-node-1x-BMG-Pro-B60-Dual b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/systems/user.conf.1-node-1x-BMG-Pro-B60-Dual
new file mode 100644
index 00000000000..f538532ee7b
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/systems/user.conf.1-node-1x-BMG-Pro-B60-Dual
@@ -0,0 +1 @@
+whisper.Offline.min_query_count = 18000
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/test.py b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/test.py
new file mode 100644
index 00000000000..0dcfb48f4f5
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/test.py
@@ -0,0 +1,125 @@
+# from numa import schedule, memory
+from vllm import LLM, SamplingParams
+import os
+import time
+
+import librosa
+import torch
+from vllm.config import CompilationConfig, CompilationLevel
+# NODE_LIST = [5]
+# OMP_NUM_THREADS = 6
+# START_CORE = 214
+# OMP_THREADS_BIND = f"{START_CORE}-{START_CORE+OMP_NUM_THREADS-1}"
+MODEL_PATH="/model/whisper-large-v3/"
+# MODEL_PATH = "/model/whisper-large-v3-w4a8g-1"
+SAMPLE = "/data/dev-all-repack/116-288045_0.wav"
+PREFILL_BATCH_SIZE=1
+DECODE_BATCH_SIZE=1
+
+# os.environ["VLLM_CPU_OMP_THREADS_BIND"]=f"{OMP_THREADS_BIND}"
+# os.environ["OMP_NUM_THREADS"]=f"{OMP_NUM_THREADS}"
+# os.environ["VLLM_CPU_KVCACHE_SPACE"]="32"
+
+def setup_profiler(enabled):
+    if not enabled:
+        return None
+    schedule = torch.profiler.schedule(wait=0, warmup=0, active=1, repeat=0)
+    DEVICE = 'xpu:0'
+    activities = [torch.profiler.ProfilerActivity.XPU]
+
+    profiler = torch.profiler.profile(
+        schedule=schedule,
+        activities=activities,
+        #debug_activities=debug_activities,
+        # on_trace_ready=torch.profiler.tensorboard_trace_handler(
+        #       'pytorch_profiler_internal',
+        #       use_gzip=True),
+        record_shapes=True,
+        profile_memory=True,
+        with_flops=True,
+        with_stack=True)
+    return profiler
+
+class DummyProfiler:
+    def start(*args, **kwargs):
+        pass
+    def step(*args, **kwargs):
+        pass
+    def stop(*args, **kwargs):
+        pass
+
+def main():
+    # profiler_prefill = DummyProfiler()
+    # profiler_decode = DummyProfiler()
+    enabled = False
+    profiler_prefill = setup_profiler(enabled)
+    profiler_decode = setup_profiler(enabled)
+    # memory.set_membind_nodes(*NODE_LIST)
+    prompt = {
+        "prompt": "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>",
+        "multi_modal_data": {
+            "audio": librosa.load(SAMPLE, sr=16000)
+        },
+    }
+    print("Audio length", len(prompt["multi_modal_data"]["audio"][0]))
+    model = LLM(
+        model=MODEL_PATH,
+        dtype="bfloat16",
+        skip_tokenizer_init=False,
+        # trust_remote_code=True,
+        tensor_parallel_size=1,
+        max_num_seqs=DECODE_BATCH_SIZE,
+        max_model_len=448,
+        # max_num_batched_tokens=800,
+        gpu_memory_utilization=0.4,
+        # num_scheduler_steps=1,
+        limit_mm_per_prompt={"audio": 1},
+        kv_cache_dtype="auto",
+        compilation_config=CompilationConfig(
+            level=CompilationLevel.PIECEWISE,
+            use_inductor=True,
+            # compile_sizes=[64,128,256],
+        )
+    )
+    sampling_params = SamplingParams(
+        temperature=0,
+        top_p=1.0,
+        max_tokens=200
+    )
+    
+    req_counter = 0
+
+    # Prefill
+    if enabled:
+        profiler_prefill.start()
+    for i in range(DECODE_BATCH_SIZE):
+        for j in range(PREFILL_BATCH_SIZE):
+            model.llm_engine.add_request(str(req_counter), prompt, sampling_params)
+            req_counter += 1
+        end = time.time()
+        model.llm_engine.step()
+        if enabled:
+            profiler_prefill.step()
+        print(f"Prefill bs={PREFILL_BATCH_SIZE} time {(time.time()-end)*1000:.2f}ms")
+    if enabled:
+        profiler_prefill.stop()
+
+    # Decode
+    results = []
+    if enabled:
+        profiler_decode.start()
+    while model.llm_engine.has_unfinished_requests():
+        end = time.time()
+        step_outputs = model.llm_engine.step()
+        if enabled:
+            profiler_decode.step()
+        print(f"Decode bs={DECODE_BATCH_SIZE} time {(time.time()-end)*1000:.2f}ms")
+        for output in step_outputs:
+            if output.finished:
+                id = int(output.request_id)
+                print(f"Finished 1, {output.outputs[0].text}")
+    if enabled:
+        profiler_decode.stop()
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/tmp b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/tmp
new file mode 100644
index 00000000000..e5c7f6581ca
--- /dev/null
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/mlperf/whisper/tmp
@@ -0,0 +1,57 @@
+py-libnuma==1.2
+nltk==3.9.1
+evaluate==0.4.3
+rouge_score==0.1.2
+pybind11==3.0.0
+ninja==1.11.1.4
+cmake==4.0.3
+wheel==0.45.1
+setuptools==80.9.0
+setuptools_scm==8.3.1
+auto-round==0.6.0
+llmcompressor==0.6.0.1
+
+# vllm reqs
+cachetools==6.1.0
+cloudpickle==3.1.1
+psutil==7.0.0
+blake3==1.0.5
+depyf==0.18.0
+lark==1.2.2
+numba==0.60.0
+outlines==0.1.11
+xgrammar==0.1.19
+py-cpuinfo==9.0.0
+einops==0.8.1
+fastapi[standard]==0.116.1
+gguf==0.17.1
+llguidance==0.7.30
+lm-format-enforcer==0.10.11
+mistral_common[opencv]==1.8.3
+openai==1.98.0
+opencv-python-headless
+opentelemetry-api==1.36.0
+opentelemetry-exporter-otlp==1.36.0
+opentelemetry-sdk==1.36.0
+opentelemetry-semantic-conventions-ai==0.4.12
+partial-json-parser==0.2.1.1.post6
+prometheus_client==0.22.1
+prometheus-fastapi-instrumentator==7.1.0
+protobuf==6.31.1
+python-json-logger==3.3.0
+pyzmq==27.0.0
+ray==2.48.0
+scipy==1.15.3
+watchfiles==1.1.0
+msgspec==0.19.0
+
+# ipex reqs
+ruamel.yaml==0.18.14
+
+torch==2.7.0+xpu
+torchaudio
+torchvision
+--extra-index-url=https://download.pytorch.org/whl/xpu
+
+oneccl_bind_pt==2.7.0+xpu
+--extra-index-url=https://pytorch-extension.intel.com/release-whl/stable/xpu/us/