diff --git a/inferencex-e2e/benchmarks/single_node/srt-slurm-recipes/musespark/sglang/h100-fp4-mtp/agentic.yaml b/inferencex-e2e/benchmarks/single_node/srt-slurm-recipes/musespark/sglang/h100-fp4-mtp/agentic.yaml
new file mode 100644
index 0000000000..3c55839fa3
--- /dev/null
+++ b/inferencex-e2e/benchmarks/single_node/srt-slurm-recipes/musespark/sglang/h100-fp4-mtp/agentic.yaml
@@ -0,0 +1,95 @@
+base:
+  schema: 2
+  name: musespark-fp4-h100-sglang-agentic
+  resources:
+    gpu_type: h100
+    gpus_per_node: 8
+  slurm:
+    time_limit: '4:00:00'
+  frontend:
+    type: sglang
+    enable_multiple_frontends: false
+  observability:
+    enabled: false
+    tachometer:
+      enabled: false
+  engine: sglang
+  roles:
+    agg:
+      nodes: 1
+      workers: 1
+      gpus: 8
+      args:
+        served-model-name: meta-models/Muse-Spark-Open-816B-A42B-NVFP4
+        trust-remote-code: true
+        tensor-parallel-size: 8
+        expert-parallel-size: 8
+        quantization: modelopt_fp4
+        moe-runner-backend: marlin
+        fp4-gemm-backend: marlin
+        attention-backend: fa3
+        kv-cache-dtype: fp8_e4m3
+        # Loaded target and draft weights require a static fraction above 0.94.
+        mem-fraction-static: 0.97
+        # Bound prefill activations while retaining the full request context.
+        chunked-prefill-size: 1024
+        # Full-attention KV must fit 256k prompts; SWA retains its 2048-token window.
+        swa-full-tokens-ratio: 0.1
+        disable-prefill-cuda-graph: true
+        # DFlash2 verifies 8 tokens per step: the current token plus 7 drafts.
+        speculative-algorithm: DFLASH
+        speculative-draft-model-path: RadixArk/Muse-Spark-Open-DFlash2
+        speculative-draft-attention-backend: fa4
+        speculative-num-draft-tokens: 8
+        mm-feature-transport: cpu
+        reasoning-parser: muse
+        tool-call-parser: muse
+        watchdog-timeout: 3600
+        enable-metrics: true
+      env:
+        PYTHONNOUSERSITE: '1'
+        PYTHONUNBUFFERED: '1'
+        # Eager sampling supports admission above the captured graph batch size.
+        SGLANG_DFLASH_EAGER_DRAFT_SAMPLER: '1'
+        # Outlast AIPerf's pooled connections past Uvicorn's keep-alive.
+        SGLANG_TIMEOUT_KEEP_ALIVE: '900'
+  health_check:
+    max_attempts: 1440
+    interval_seconds: 5
+  benchmark:
+    env:
+      # The gated Hub repo is unreadable to the client; use the staged checkpoint.
+      TOKENIZER: /model
+      # Warmup can leave bytes unacknowledged past AIPerf's 30 s default.
+      AIPERF_HTTP_TCP_USER_TIMEOUT: '900000'
+      AIPERF_REQUIRED_SERVER_METRIC_PREFIX: 'sglang:'
+      AGENTIC_WARMUP_GRACE_PERIOD: '3600'
+
+zip_override_tp8:
+  roles:
+    agg:
+      args:
+        # Eager batches above the decode graphs OOM in the MoE with ~1 GB free.
+        max-running-requests: [2, 4, 8, 8, 8]
+        # Each graph slot reserves seven full-context int32 page tables, and
+        # bs=16 graphs leave too little headroom for the vision encoder.
+        cuda-graph-max-bs-decode: [2, 4, 8, 8, 8]
+  benchmark:
+    env:
+      CONC: ['1', '2', '4', '8', '16']
+      KV_OFFLOADING: none
+
+# The GPU KV pool is a few GB per rank, so agentic prefixes evict between turns;
+# HiCache keeps them in host DRAM instead of recomputing them.
+zip_override_tp8_hicache:
+  roles:
+    agg:
+      args:
+        max-running-requests: [4, 8, 8]
+        cuda-graph-max-bs-decode: [4, 8, 8]
+        enable-hierarchical-cache: true
+        hicache-size: 160
+  benchmark:
+    env:
+      CONC: ['2', '4', '8']
+      KV_OFFLOADING: dram
diff --git a/inferencex-e2e/configs/nvidia-master.yaml b/inferencex-e2e/configs/nvidia-master.yaml
index ffd7a896bb..1fc173c6a4 100644
--- a/inferencex-e2e/configs/nvidia-master.yaml
+++ b/inferencex-e2e/configs/nvidia-master.yaml
@@ -9696,3 +9696,19 @@ musespark-fp4-b300-sglang-agentic-dflash:
     agentic-coding:
     - search-space:
       - { tp: 4, ep: 4, kv-offloading: none, spec-decoding: draft_model, conc-list: [1, 2, 4, 8, 16, 24, 32, 48, 64, 128], srt-recipe: agentic.yaml }
+
+musespark-fp4-h100-sglang-agentic-dflash:
+  image: lmsysorg/sglang:dev-muse-spark-open@sha256:8cfc5a72cb296771f5f8a4a4688cb6d05685610a830cbb616fa4ab0eb7bb2b0a
+  model: meta-models/Muse-Spark-Open-816B-A42B-NVFP4
+  model-prefix: musespark
+  runner: cluster:h100-dsxe
+  precision: fp4
+  framework: sglang
+  multinode: false
+  srt-recipe-dir: musespark/sglang/h100-fp4-mtp
+  scenarios:
+    agentic-coding:
+    - dram-utilization: 0.80
+      search-space:
+      - { tp: 8, ep: 8, kv-offloading: none, spec-decoding: draft_model, conc-list: [1, 2, 4, 8, 16], srt-recipe: agentic.yaml }
+      - { tp: 8, ep: 8, kv-offloading: dram, kv-offload-backend: { name: hicache }, spec-decoding: draft_model, conc-list: [2, 4, 8], srt-recipe: agentic.yaml }
diff --git a/inferencex-e2e/configs/runners.yaml b/inferencex-e2e/configs/runners.yaml
index e5e775bd8b..01dfc45c9a 100644
--- a/inferencex-e2e/configs/runners.yaml
+++ b/inferencex-e2e/configs/runners.yaml
@@ -362,6 +362,7 @@ clusters:
       entries:
         DeepSeek-V4.1-Flash: {root: data-models, dir: DeepSeek-V4.1-Flash}
         MiniMax-M3-MXFP8: {root: data-models, dir: MiniMax-M3-MXFP8}
+        Muse-Spark-Open-816B-A42B-NVFP4: {root: data-models, dir: Muse-Spark-Open-816B-A42B-NVFP4}
         Qwen3.5-397B-A17B-FP8: {root: data-models, dir: Qwen3.5-397B-A17B-FP8}
     scheduler: slurm
     slurm:
diff --git a/inferencex-e2e/perf-changelog.yaml b/inferencex-e2e/perf-changelog.yaml
index c5da284820..8ec368892e 100644
--- a/inferencex-e2e/perf-changelog.yaml
+++ b/inferencex-e2e/perf-changelog.yaml
@@ -9984,6 +9984,15 @@
     - "DEP2 and DEP4 arms: MegaMoE all-to-all with flashinfer_mxfp4 runner, trtllm DSv4 attention, HiCache (ratio 4 on DEP2, 2 on DEP4), shortest-prefill-first, DP spec-prefill coordination, and sglang-router 0.3.2 consistent hashing with correlation-ID affinity."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4094
 
+- config-keys:
+    - musespark-fp4-h100-sglang-agentic-dflash
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Add Muse Spark Open NVFP4 AgentX on H100 at TP8/EP8 with upstream Marlin dense/MoE kernels, FA3 target attention, FA4 draft attention, and FP8 KV."
+    - "Sweep concurrency 1, 2, 4, 8, and 16 with DFlash2, GPU-resident KV, a 0.97 static memory fraction, 1024-token prefill chunks, a 0.1 SWA/full KV token ratio, and prefill CUDA graphs disabled; cap decode graphs and running requests at batch 8, since eager batches above graph capacity OOM with ~1 GB free per GPU; concurrency 16 measures queueing at that cap. Use the committed golden acceptance curve for throughput and real verification for evals."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4149
+
 - config-keys:
     - musespark-fp4-mi355x-sglang-agentic-dflash
   scenario-type:
@@ -9995,3 +10004,12 @@
     - "Synthetic acceptance follows the committed musespark_dflash golden curve (#4138, #4139): AL 4.05 at 7 draft tokens, thinking_on."
     - "The server loads the checkpoint and draft from the pre-seeded shared HF cache with HF_HUB_OFFLINE=1, so concurrent points make no Hub API calls."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4127
+
+- config-keys:
+    - musespark-fp4-h100-sglang-agentic-dflash
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Add a HiCache host-DRAM KV offload arm at concurrency 2, 4 and 8 with 160 GB per rank, alongside the GPU-resident sweep; the few-GB GPU KV pool left by 67 GB of weights per H100 evicts agentic prefixes between turns."
+    - "Keep the batch-8 cap on running requests and decode graphs from the GPU-resident sweep."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4181