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2 changes: 1 addition & 1 deletion configs/nvidia-master.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -7777,7 +7777,7 @@ minimaxm3-fp4-b300-trtllm-agentic-mtp:
# the same 3 TB AgentX ceiling before the proportional-GPU rule is applied.
# GPU-resident points receive a zero budget.
minimaxm3-fp4-b200-vllm-agentic-mtp:
image: vllm/vllm-openai:nightly-1dc464d42681d22f38caf1fdc1eb632dc4421c45
image: vllm/vllm-openai:nightly-8a728663c1c3eeace834a95f5654fa653cc1998c
model: nvidia/MiniMax-M3-NVFP4
model-prefix: minimaxm3
runner: cluster:b200-nscale
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9 changes: 9 additions & 0 deletions perf-changelog.yaml
Original file line number Diff line number Diff line change
Expand Up @@ -6921,3 +6921,12 @@
- "Pick up the latest automatic ROCm DeepSeek-V4 optimizations, including fused mHC post/pre plus RMSNorm, gfx950 C4A top-k dispatch, fused C4 compressor GEMMs, fused SWA q/kv RMSNorm plus q FP8 quantization, and medium-batch cooperative top-k tuning."
- "Keep the existing VLLM_ROCM_USE_AITER=1, VLLM_ROCM_USE_AITER_MOE=1, and --moe-backend aiter settings, and explicitly add VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS=1 plus VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=INT4 to both STP and MTP paths. The current checkpoint's shared-expert path does not satisfy the latest vLLM fusion conditions, so that fusion flag self-disables while preserving recipe parity."
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2792

- config-keys:
- minimaxm3-fp4-b200-vllm-agentic-mtp
scenario-type:
- agentic-coding
description:
- "Update vLLM image from vllm/vllm-openai:nightly-1dc464d42681d22f38caf1fdc1eb632dc4421c45 (2026-08-30 nightly) to vllm/vllm-openai:nightly-d9105ea8001e0a6d77a96327d17515bb5791fb36 (2026-09-07 nightly, digest sha256:254eebf919e8b7b0d530d97fccc606c36f380ff6724d64bc190951bec1aee838, tag commit vllm-project/vllm@d9105ea8). Docker Hub last pushed the tag at 2026-09-07T06:16:01Z."
- "Re-pin from vllm/vllm-openai:nightly-d9105ea8001e0a6d77a96327d17515bb5791fb36 (2026-09-07 nightly) to vllm/vllm-openai:nightly-8a728663c1c3eeace834a95f5654fa653cc1998c (2026-09-04 nightly, digest sha256:f5df5cc3302b5f404848c4eca88d7bf7ed5226e151c056da22816d7734644d67; Docker Hub last pushed 2026-09-04T06:18:14Z, tag commit vllm-project/vllm@8a728663). Nightlies from 2026-09-05 onward carry vllm-project/vllm@4ee259551 (Sync FA with upstream, #54819), which moves vllm-flash-attn to 506341a1; on Blackwell the EAGLE3 draft runs FLASH_ATTN through the FA4 CuTe path and, with the fp8 KV cache, its descale tensors now fail to_cute_tensor with RuntimeError: Expected strides[leading_dim] == 1, but got 0 during CUDA-graph memory profiling, so the engine never starts (B200 run 34168437161). The 2026-09-04 nightly is the newest build still on vllm-flash-attn 06bdd47c, the same FA pin as the recipe's previous 2026-08-30 image, and the same vllm commit the MI355X MiniMax-M3 vLLM AgentX recipe already runs (nightly-8a728663). No vllm fix has landed on main as of 2026-09-08T03:00Z."
pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2860
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