Files
spark-vllm-docker/recipes/nemotron-3-nano-nvfp4.yaml
2026-02-09 14:33:35 -08:00

48 lines
1.3 KiB
YAML

# Recipe: Nemotron-3-Nano-NVFP4
# Nemotron-3-Nano model with NVFP4 quantization support
# Currently can only be run in solo mode, cluster mode fails with error
recipe_version: "1"
name: Nemotron-3-Nano-NVFP4
description: vLLM serving Nemotron-3-Nano-NVFP4 on a SINGLE NODE ONLY!
# HuggingFace model to download (optional, for --download-model)
model: nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4
# Container image to use
container: vllm-node
# This model can only run on single node (solo)
solo_only: true
# No mods required
mods:
- mods/nemotron-nano
# Default settings (can be overridden via CLI)
defaults:
port: 8000
host: 0.0.0.0
tensor_parallel: 1
gpu_memory_utilization: 0.7
max_model_len: 131072
# Environment variables
env:
VLLM_USE_FLASHINFER_MOE_FP4: 1
VLLM_FLASHINFER_MOE_BACKEND: "throughput"
# The vLLM serve command template
command: |
vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 \
--max-model-len {max_model_len} \
--port {port} --host {host} \
--trust-remote-code \
--enable-auto-tool-choice \
--tool-call-parser qwen3_coder \
--reasoning-parser-plugin nano_v3_reasoning_parser.py \
--reasoning-parser nano_v3 \
--kv-cache-dtype fp8 \
--load-format fastsafetensors \
--gpu-memory-utilization {gpu_memory_utilization}