Oseltamivir · Oct 16, 2026 · Oct 15, 2026 · Oct 15, 2026 · Oct 16, 2026
@@ -0,0 +1,69 @@
+base:
+  schema: 2
+  name: musespark-fp4-h200-sglang-agentic
+  resources:
+    gpu_type: h200
+    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
+        mem-fraction-static: 0.85
+        # 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'
+        # 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:
+        max-running-requests: [2, 4, 8, 16, 32, 48, 64, 96, 128]
+        cuda-graph-max-bs-decode: [2, 4, 8, 16, 32, 48, 64, 96, 128]
+  benchmark:
+    env:
+      CONC: ['1', '2', '4', '8', '16', '24', '32', '48', '64']
@@ -8365,6 +8365,20 @@ musespark-fp4-b200-sglang-agentic-dflash:
       - { tp: 8, ep: 8, kv-offloading: none, spec-decoding: draft_model, conc-list: [1, 2, 4, 8, 12], srt-recipe: agentic.yaml }
       - { tp: 8, ep: 8, kv-offloading: dram, kv-offload-backend: { name: hicache }, spec-decoding: draft_model, conc-list: [16, 24, 32, 48], srt-recipe: agentic.yaml }
 
+musespark-fp4-h200-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:h200-dgxc
+  precision: fp4
+  framework: sglang
+  multinode: false
+  srt-recipe-dir: musespark/sglang/h200-fp4-mtp
+  scenarios:
+    agentic-coding:
+    - search-space:
+      - { tp: 8, ep: 8, kv-offloading: none, spec-decoding: draft_model, conc-list: [1, 2, 4, 8, 16, 24, 32, 48, 64], srt-recipe: agentic.yaml }
+
 dsv41flash-fp4-b300-vllm-agentic-dspark:
   image: vllm/vllm-openai:nightly-dev-x86_64-cu130-ac9126e58aa7
   model: deepseek-ai/DeepSeek-V4.1-Flash

@@ -396,6 +396,7 @@ clusters:
         GLM-5.2-FP8: {root: models, dir: GLM-5.2-FP8}
         DeepSeek-V4-Pro-0813: {root: hf-hub-cache, dir: DeepSeek-V4-Pro}
         Kimi-K3: {root: hf-hub-cache, dir: Kimi-K3}
+        Muse-Spark-Open-816B-A42B-NVFP4: {root: hf-hub-cache, dir: Muse-Spark-Open-816B-A42B-NVFP4}
         Qwen3.5-397B-A17B-FP8: {root: hf-hub-cache, dir: Qwen3.5-397B-A17B-FP8}
     scheduler: slurm
     slurm:

@@ -32,6 +32,7 @@ class Override:
     served_name: str | None = None
     require_config: bool = False
     hub: bool = False
+    single_node_staged: bool = False
 
 
 OVERRIDES: dict[str, tuple[Override, ...]] = {
@@ -74,6 +75,7 @@ class Override:
         ),
     ),
     "h200-dgxc": (
+        Override(Match(model_glob="*/Muse-Spark-Open-816B-A42B-NVFP4"), single_node_staged=True),
         Override(Match(any_of("dsv4")), require_config=True),
         Override(
             Match(any_of("dsr1"), any_of("fp8"), any_of("dynamo-trt")),
@@ -182,7 +184,9 @@ def job_env(cluster: Cluster, request: LaunchRequest, served: str | None) -> dic
 def single_node_model_path(cluster: Cluster, request: LaunchRequest) -> str:
     """What the single-node recipe's ``hf:<MODEL>`` serves: the staged checkpoint, or the Hub."""
     srt = slurm_settings(cluster).srt_slurm
-    staged = srt is not None and srt.single_node_models == "staged"
+    staged = (srt is not None and srt.single_node_models == "staged") or (
+        _override(cluster, request, "single_node_staged") is True
+    )
     model = checkpoint(cluster, request) if staged else None
     return str(host_path(cluster, model)) if model is not None else f"hf:{request.model}"
 

@@ -96,6 +96,15 @@ def test_models_resolve_by_basename_with_overrides(tmp_path, monkeypatch, env, p
     assert single_node_model_path(cluster(tmp_path, "hub"), point) == f"hf:{env['MODEL']}"
 
 
+def test_single_node_staging_can_be_selected_for_one_workload(tmp_path, monkeypatch):
+    monkeypatch.setitem(models.OVERRIDES, "c", (
+        Override(Match(model_glob="org/M"), single_node_staged=True),
+    ))
+    c = cluster(tmp_path, "hub")
+    assert single_node_model_path(c, request(MODEL="org/M")) == str(tmp_path / "nvme/m")
+    assert single_node_model_path(c, request(MODEL="org/S")) == "hf:org/S"
+
+
 def test_a_checkpoint_that_must_be_readable_fails_before_submission(tmp_path, monkeypatch):
     monkeypatch.setitem(models.OVERRIDES, "c", OVERRIDES)
     with pytest.raises(LaunchError, match="no readable .*/s/config.json"):

@@ -9893,6 +9893,16 @@
     - "No change to the Inferact/Kimi-K3-DSpark draft's precision: K3DSparkModel and get_draft_quant_config() are unchanged between the two images, so the draft keeps BF16 weights and an fp8 KV cache."
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/3951
 
+- config-keys:
+    - musespark-fp4-h200-sglang-agentic-dflash
+  scenario-type:
+    - agentic-coding
+  description:
+    - "Add Muse Spark Open NVFP4 AgentX on H200 with upstream SGLang and DFlash2 speculative decoding at TP8/EP8."
+    - "Use Marlin W4A16 dense and MoE kernels, FA3 target attention, FA4 draft attention, FP8 KV, and an 8-token DFlash verification block with acceptance selected from the committed golden curve."
+    - "Sweep concurrency 1, 2, 4, 8, 16, 24, 32, 48, and 64 with GPU-resident KV and no layernorm sequence parallelism or MegaMoE."
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/4148
+
 - config-keys:
     - musespark-fp4-b200-sglang-agentic-dflash
   scenario-type: