#!/usr/bin/env python3 """ DeepSeek V4 Pro NVFP4 — vLLM OpenAI-compatible server. Run from the venv on the B200 node: source /root/nvidia-meeting/venv/bin/activate python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/serve_vllm.py Or in the background: nohup python3 /root/nvidia-meeting/deepseek-v4-quant/scripts/serve_vllm.py \ > /root/nvidia-meeting/vllm_serve.log 2>&1 & """ import subprocess import sys MODEL = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4" # These flags are critical for V4 — do not change without understanding why: # --trust-remote-code V4 needs custom modeling code # --kv-cache-dtype fp8 Match our kv_cache_qformat=fp8_cast quantization # --block-size 256 V4 recommended block size # --enable-expert-parallel Distribute expert computation across GPUs (critical for 256-expert MoE) # --tensor-parallel-size 8 8× B200 # --compilation-config CUDA graphs for throughput — FULL_AND_PIECEWISE + all custom ops # --attention_config FP4 indexer cache for V4 MLA attention # --moe-backend deep_gemm_mega_moe — optimized MoE kernel for Blackwell # --tokenizer-mode deepseek_v4 — V4-specific tokenizer # --tool-call-parser deepseek_v4 — native tool calling # --enable-auto-tool-choice Auto tool choice for function calling # --reasoning-parser deepseek_v4 — reasoning/thinking output parsing # --speculative_config MTP speculative decoding (2 speculative tokens) cmd = [ sys.executable, "-m", "vllm.entrypoints.openai.api_server", "--model", MODEL, "--trust-remote-code", "--kv-cache-dtype", "fp8", "--block-size", "256", "--enable-expert-parallel", "--tensor-parallel-size", "8", "--compilation-config", '{"cudagraph_mode":"FULL_AND_PIECEWISE", "custom_ops":["all"]}', "--attention_config.use_fp4_indexer_cache=True", "--moe-backend", "deep_gemm_mega_moe", "--tokenizer-mode", "deepseek_v4", "--tool-call-parser", "deepseek_v4", "--enable-auto-tool-choice", "--reasoning-parser", "deepseek_v4", "--speculative_config", '{"method":"mtp","num_speculative_tokens":2}', "--host", "0.0.0.0", "--port", "8000", ] print(f"Starting vLLM server for {MODEL}") print(f"Command: {' '.join(cmd)}") print(f"Log: /root/nvidia-meeting/vllm_serve.log") print() sys.exit(subprocess.call(cmd))