serve a pre-quantized awq or gptq checkpoint with vllm
domain: docs.vllm.ai · 5 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Confirm the checkpoint on Hugging Face Hub is pre-quantized in AWQ or GPTQ/GPTQModel format by checking the model card
For AWQ, install the backend with pip install autoawq and load with LLM(model=..., quantization="auto_awq") or pass --quantization awq to vllm serve
For GPTQ, install pip install auto-gptq (or use a GPTQModel-produced checkpoint) and pass --quantization gptq
Check the vLLM quantization docs page for the current list of supported methods and any Marlin/Machete kernel requirements for your GPU generation
Benchmark against the unquantized model to confirm the expected memory reduction and any throughput/accuracy tradeoff before promoting to production
Known gotchas
vLLM does not bundle GPTQ/AWQ backends by default — the required pip package (auto-gptq or autoawq) must be installed separately or the server fails to load the model
GPTQModel-produced checkpoints get better performance on Ampere/Hopper GPUs via Marlin/Machete kernels than plain GPTQ; confirm which kernel path is actually selected
Passing the wrong --quantization value for the actual checkpoint format (e.g. gptq for an awq checkpoint) fails at load time — verify the model card before setting the flag
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