diff --git a/configs/nvidia-master.yaml b/configs/nvidia-master.yaml index f10d232137..a60792bd68 100644 --- a/configs/nvidia-master.yaml +++ b/configs/nvidia-master.yaml @@ -7234,7 +7234,7 @@ minimaxm3-fp4-b300-vllm-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-5e35a6f4f9bbc217c599692157ca985c894373f7 + image: vllm/vllm-openai:nightly-ac7509e2b1db40fec2f03dde1ed4e9dfdc2338c9 model: nvidia/MiniMax-M3-NVFP4 model-prefix: minimaxm3 runner: cluster:b200-dgxc @@ -7245,8 +7245,11 @@ minimaxm3-fp4-b200-vllm-agentic-mtp: agentic-coding: - dram-utilization: 0.683 search-space: - - { tp: 4, spec-decoding: mtp, kv-offloading: none, conc-list: [1, 2, 5, 8, 10, 12, 15, 20] } - - { tp: 4, spec-decoding: mtp, kv-offloading: dram, kv-offload-backend: { name: vllm-simple }, conc-list: [20, 30, 40] } + - { tp: 8, spec-decoding: mtp, kv-offloading: none, conc-list: [1] } + - { tp: 4, spec-decoding: mtp, kv-offloading: none, conc-list: [1, 5, 10, 15, 20] } + - dram-utilization: 1.0 + search-space: + - { tp: 4, spec-decoding: mtp, kv-offloading: dram, kv-offload-backend: { name: vllm-simple }, conc-list: [15, 20, 25, 30, 32, 34, 36, 38, 40] } # Preserve the B200 TP4 search space and add the GB200 Pareto candidates found # by direct DEP and P/D tuning. minimaxm3-fp4-gb200-dynamo-vllm-agentic-agg-mtp: diff --git a/perf-changelog.yaml b/perf-changelog.yaml index 24cad03446..889d4db484 100644 --- a/perf-changelog.yaml +++ b/perf-changelog.yaml @@ -5999,3 +5999,11 @@ - "Add the tuned GB200 MiniMax-M3 FP4 AgentX frontier with EAGLE3, the B200 TP4 baseline, DEP4/DEP8, SimpleCPU offload, and KV-routed P/D." - "Use full-decode-only CUDA graphs for TP4 stability." pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2609 + +- config-keys: + - minimaxm3-fp4-b200-vllm-agentic-mtp + scenario-type: + - agentic-coding + description: + - "Refresh the MiniMax-M3 B200 AgentX submission: no-offload TP8 C1 and TP4 C1/C5/C10/C15/C20, plus a TP4 SimpleCPU KV-offload sweep at C15–C40 using the full-capacity DRAM budget." + pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/2611