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+engine = "vllm"
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+name = "MiniMax M3 MXFP8 (vLLM)"
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+docker_image = "vllm/vllm-openai:minimax-m3"
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+model = "MiniMaxAI/MiniMax-M3-MXFP8"
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+parent_model = "MiniMaxAI/MiniMax-M3"
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+required_nodes = 4
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+tensor_parallel_size = 4
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+pipeline_parallel_size = 1
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+distributed_backend = "mp"
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+
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+# MXFP8 quantized MiniMax M3 (~440B params, ~23B active).
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+# 4 nodes × 1 GPU (DGX Spark GB10 Blackwell), TP=4 across nodes.
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+# MSA sparse attention requires --block-size 128.
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+# Text-only mode skips the vision encoder to save VRAM.
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+
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+#entrypoint = ["vllm", "serve"]
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+
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+args = [
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+ "--block-size", "128",
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+ "--kv-cache-dtype", "fp8",
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+ "--gpu-memory-utilization", "0.85",
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+ "--load-format", "safetensors",
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+ "--enable-prefix-caching",
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+ "--tool-call-parser", "minimax_m3",
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+ "--reasoning-parser", "minimax_m3",
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+ "--enable-auto-tool-choice",
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+ "--language-model-only",
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+ "--enable-expert-parallel",
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+ "--trust-remote-code",
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+ "--served-model-name", "minimax-m3", "default",
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+]
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+
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+[env]
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+# M3 supports thinking via chat_template_kwargs.thinking_mode
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+# enabled / adaptive / disabled — pass per-request in extra_body
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+SAFETENSORS_FAST_GPU = "1"
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+VLLM_ALLOW_LONG_MAX_MODEL_LEN = "1"
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+VLLM_USE_B12X_MOE = "1"
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