[NVIDIA] feat: MiniMax M3 Day 0 support B300

.github/configs/nvidia-master.yaml

@@ -11678,3 +11678,40 @@ minimaxm2.5-fp8-gb300-dynamo-vllm:
           tp: 4
           ep: 4
           dp-attn: true
+
+# MiniMax-M3 day-zero (https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3).
+# 427B total / 26B active MoE with MSA sparse attention; MXFP8 checkpoint
+# (MiniMaxAI/MiniMax-M3-MXFP8, ~444 GB) quantized by NVIDIA — native MX tensor
+# cores on Blackwell. M3 support has not shipped in a stable vLLM release;
+# the dedicated vllm/vllm-openai:minimax-m3 image is built from the m3_release
+# branch (vllm-project/vllm#45381). --block-size 128 is mandatory (MSA
+# sparse/index cache alignment). Weights are NOT SRE-staged; b300 falls back
+# to writable /data/models (see launch_b300-nv.sh MODEL_PATH split).
+minimaxm3-fp8-b300-vllm:
+  image: vllm/vllm-openai:minimax-m3
+  model: MiniMaxAI/MiniMax-M3-MXFP8
+  model-prefix: minimaxm3
+  runner: b300
+  precision: fp8
+  framework: vllm
+  multinode: false
+  scenarios:
+    fixed-seq-len:
+    - isl: 1024
+      osl: 1024
+      search-space:
+      - { tp: 8, conc-start: 1, conc-end: 64 }
+      - { tp: 8, ep: 8, conc-start: 1, conc-end: 512 }
+      - { tp: 4, conc-start: 1, conc-end: 64 }
+      - { tp: 4, ep: 4, conc-start: 64, conc-end: 512 }
+      # tp2 fits MXFP8 weights (~222 GB/GPU of 288) but KV headroom is thin;
+      # 1k1k only, drop if it OOMs at the high end.
+      - { tp: 2, ep: 2, conc-start: 16, conc-end: 128 }
+      - { tp: 8, ep: 8, dp-attn: true, conc-start: 256, conc-end: 1024 }
+    - isl: 8192
+      osl: 1024
+      search-space:
+      - { tp: 8, conc-start: 1, conc-end: 64 }
+      - { tp: 8, ep: 8, conc-start: 1, conc-end: 512 }
+      - { tp: 4, conc-start: 1, conc-end: 128 }
+      - { tp: 8, ep: 8, dp-attn: true, conc-start: 128, conc-end: 512 }

benchmarks/single_node/fixed_seq_len/minimaxm3_fp8_b300.sh

@@ -0,0 +1,109 @@
+#!/usr/bin/env bash
+
+# MiniMax-M3 MXFP8 B300 single-node vLLM recipe
+# (https://recipes.vllm.ai/MiniMaxAI/MiniMax-M3). Same shape as the B200
+# script, but follows the b300 launcher's MODEL/MODEL_PATH split:
+# launch_b300-nv.sh keeps MODEL as the HF id and points MODEL_PATH at
+# /data/models/<basename> (writable NFS) for models not in the SRE-staged
+# /scratch/models list — MiniMax-M3 is not staged. --block-size 128 is
+# mandatory (MSA sparse/index cache); the benchmark is text-only, so
+# --language-model-only frees the vision encoder's VRAM.
+
+source "$(dirname "$0")/../../benchmark_lib.sh"
+
+check_env_vars \
+    MODEL \
+    TP \
+    EP_SIZE \
+    DP_ATTENTION \
+    CONC \
+    ISL \
+    OSL \
+    MAX_MODEL_LEN \
+    RANDOM_RANGE_RATIO \
+    RESULT_FILENAME
+
+# `hf download` creates the target dir if missing and is itself idempotent.
+# When MODEL_PATH is unset (stand-alone runs), fall back to the HF_HUB_CACHE.
+# Either way, MODEL_PATH is what the server is launched with.
+if [[ -n "${MODEL_PATH:-}" ]]; then
+    if [[ ! -d "$MODEL_PATH" || -z "$(ls -A "$MODEL_PATH" 2>/dev/null)" ]]; then
+        hf download "$MODEL" --local-dir "$MODEL_PATH"
+    fi
+else
+    hf download "$MODEL"
+    export MODEL_PATH="$MODEL"
+fi
+
+if [[ -n "$SLURM_JOB_ID" ]]; then
+  echo "JOB $SLURM_JOB_ID running on $SLURMD_NODENAME"
+fi
+
+nvidia-smi
+
+SERVER_LOG=/workspace/server.log
+
+# 444 GB of MXFP8 weights off shared FS; engine startup can exceed the
+# default 600s readiness window.
+export VLLM_ENGINE_READY_TIMEOUT_S=3600
+
+if [ "${DP_ATTENTION}" = "true" ]; then
+  PARALLEL_ARGS="--tensor-parallel-size=1 --data-parallel-size=$TP --enable-expert-parallel"
+elif [ "$EP_SIZE" -gt 1 ]; then
+  PARALLEL_ARGS="--tensor-parallel-size=$TP --enable-expert-parallel"
+else
+  PARALLEL_ARGS="--tensor-parallel-size=$TP"
+fi
+
+# Fixed-seq-len runs don't need graphs past the request concurrency: capture
+# up to the next power of two >= CONC, capped at vLLM's 2048 ceiling.
+CAPTURE_SIZE=4
+while (( CAPTURE_SIZE < CONC )); do CAPTURE_SIZE=$((CAPTURE_SIZE * 2)); done
+(( CAPTURE_SIZE > 2048 )) && CAPTURE_SIZE=2048
+
+if [ "${EVAL_ONLY}" = "true" ]; then
+    setup_eval_context
+    MAX_MODEL_LEN="$EVAL_MAX_MODEL_LEN"
+fi
+# Start GPU monitoring (power, temperature, clocks every second)
+start_gpu_monitor
+
+set -x
+vllm serve "$MODEL_PATH" --served-model-name "$MODEL" --host 0.0.0.0 --port $PORT \
+$PARALLEL_ARGS \
+--gpu-memory-utilization 0.90 \
+--max-model-len $MAX_MODEL_LEN \
+--block-size 128 \
+--language-model-only \
+--max-cudagraph-capture-size $CAPTURE_SIZE \
+--max-num-batched-tokens "$((ISL * 2 ))" \
+--stream-interval 20 --no-enable-prefix-caching \
+--trust-remote-code > $SERVER_LOG 2>&1 &
+
+SERVER_PID=$!
+
+# Wait for server to be ready
+wait_for_server_ready --port "$PORT" --server-log "$SERVER_LOG" --server-pid "$SERVER_PID"
+
+run_benchmark_serving \
+    --model "$MODEL" \
+    --port "$PORT" \
+    --backend vllm \
+    --input-len "$ISL" \
+    --output-len "$OSL" \
+    --random-range-ratio "$RANDOM_RANGE_RATIO" \
+    --num-prompts "$((CONC * 10))" \
+    --max-concurrency "$CONC" \
+    --result-filename "$RESULT_FILENAME" \
+    --result-dir /workspace/ \
+    --trust-remote-code
+
+# After throughput, run evaluation only if RUN_EVAL is true
+if [ "${RUN_EVAL}" = "true" ]; then
+    run_eval --framework lm-eval --port "$PORT"
+    append_lm_eval_summary
+fi
+
+# Stop GPU monitoring
+stop_gpu_monitor
+set +x

perf-changelog.yaml

@@ -3636,3 +3636,13 @@
     - "Add MiniMax-M2.5 FP4 (NVFP4) B300 TensorRT-LLM benchmark (model: nvidia/MiniMax-M2.5-NVFP4)"
     - "Image: nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc18"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1712
+
+- config-keys:
+    - minimaxm3-fp8-b300-vllm
+  description:
+    - "Initial submission: MiniMax-M3 MXFP8 day-zero single-node vLLM benchmark on B300 (model: MiniMaxAI/MiniMax-M3-MXFP8, 427B total / 26B active MoE with MSA sparse attention)"
+    - "Image: vllm/vllm-openai:minimax-m3 (already the cu130 build; M3 support is unmerged upstream — vllm-project/vllm#45381)"
+    - "--block-size 128 is mandatory (MSA sparse/index cache alignment); --language-model-only skips the vision encoder for text-only throughput; conc-scaled --max-cudagraph-capture-size"
+    - "Layouts: TP8 and TP4 (latency), TP4+EP4 / TP8+EP8 (TEP throughput), tp2-ep2, TP8+EP8 dp-attn (DEP) across 1k1k and 8k1k"
+    - "Serves from the launch_b300-nv.sh MODEL/MODEL_PATH split (model not in the SRE-staged /scratch/models list -> writable /data/models)"
+  pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/1724