Serve NVIDIA Nemotron 3 Ultra yourself for high-throughput agents (vLLM)
Stand up the NVFP4 Nemotron 3 Ultra checkpoint as an OpenAI-compatible endpoint for fast, long-running agent loops, validated serve flags + endpoint.
Run this workflow
CI-verified, 2/2 fixtures passing.
Build this with your agent
One copy-paste hands Claude Code, Codex, or Cursor the full recipe, steps included, nothing to fetch.
Intended Use
Teams that want throughput and a fully open, fine-tunable stack over a single coding leaderboard. CI validates the serve command pins the NVFP4 checkpoint with the documented nemotron_v3 reasoning parser, qwen3_coder tool parser, and triton mamba backend, and that the agent endpoint (targeting the served model name) is well-formed. No GPU, no model call. Serving and agent work are fenced.
Not for
- Picking it purely as a coding champion, it publishes no SWE-Bench Pro number; M3 and GLM-5.1 have the clearer coding evidence
- Single-GPU, this is server-class (8x B200 or 8x H100 NVFP4; BF16 needs 8x B200 / 16x H100 / 8x H200)
The Stack
Tested Against
vllm Nemotron 3 Ultra blog + cookbook (2026-06)vllm/vllm-openai:v0.22.0node@20.xSide effects & data flow
- Network
- none, local only
- Writes
- ./serve.sh, ./endpoint.json
- Credentials
- none required
Prerequisites
- A multi-GPU node (8x B200 or 8x H100 NVFP4) or a rented GPU instance, to actually serve it
Steps
- 1
Write the serve command + agent endpoint, and validate them
This is NVIDIA's own 8x B200 NVFP4 example; the BF16 path and full flag set are in the Nemotron vLLM cookbook. Note --served-model-name, so the agent targets that name (not the checkpoint path). CI checks the serve command pins the NVFP4 checkpoint with the documented parsers and mamba backend, and the endpoint is well-formed.
cat > serve.sh <<'SH' docker pull vllm/vllm-openai:v0.22.0 && VLLM_USE_FLASHINFER_MOE_FP4=1 vllm serve nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 --served-model-name nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B --tensor-parallel-size 8 --kv-cache-dtype fp8 --max-model-len 262144 --reasoning-parser nemotron_v3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --speculative_config.method mtp --speculative_config.num_speculative_tokens 5 --mamba-backend triton SH cat > endpoint.json <<'JSON' { "base_url": "http://localhost:8000/v1", "api_key": "EMPTY", "model": "nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B" } JSON node -e 'const fs=require("fs");const s=fs.readFileSync("serve.sh","utf8");const e=JSON.parse(fs.readFileSync("endpoint.json","utf8"));const need=["nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4","--reasoning-parser nemotron_v3","--tool-call-parser qwen3_coder","--mamba-backend triton"];for(const t of need){if(!s.includes(t)){console.log("BAD: serve command missing "+t);process.exit(1)}}if(!String(e.base_url).includes("localhost:8000/v1")){console.log("BAD: endpoint base_url");process.exit(1)}if(e.api_key!=="EMPTY"){console.log("BAD: endpoint api_key should be EMPTY");process.exit(1)}if(e.model!=="nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B"){console.log("BAD: endpoint model should match --served-model-name");process.exit(1)}console.log("config OK: serve pins the NVFP4 checkpoint + nemotron_v3/qwen3_coder parsers + triton mamba backend, endpoint targets the served model name")' - 2
Point your agent at it (the model step, not checked by CI)
Use base_url http://localhost:8000/v1, key EMPTY, and model nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B (the --served-model-name, not the checkpoint path). The serving and agent loops are fenced.
Eval, 2 fixtures
Last passed: verified todayserve-okcontainstimeout 30s · max $0Expected:
config OK: serve pins the NVFP4 checkpoint + nemotron_v3/qwen3_coder parsers + triton mamba backend, endpoint targets the served model nameclean-exitexit_codetimeout 30s · max $0Expected:
0
Results
The throughput-and-openness pick: a 550B Mamba-Transformer hybrid shipped with its weights, data, and training recipes, with vLLM day-0 support. NVIDIA claims roughly 30% cost savings vs other open models. It does not publish a SWE-Bench Pro score.
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