Solutions to optimize the speed of model inference
To improve the inference speed of GPT OSS models, we can start from both hardware configuration and parameter optimization:
- Hardware Selection: For large models such as gpt-oss-120b, it is recommended to use an H100 GPU or hardware that supports MXFP4 quantization (e.g. RTX 50xx series) with a Triton kernel installed (
uv pip install git+https://github.com/triton-lang/triton.git@main#subdirectory=python/triton_kernels) to enable quantized acceleration - Framework Integration: Deployment using vLLM (
vllm serve openai/gpt-oss-20b), and its sequential batch processing characteristics increase throughput - parameterization: in
generate()medium term limitmax_new_tokenslength, and enabledo_sample=FalseTurn off random sampling - device mapping: To ensure that
device_map='auto'Correctly assign model layers to available devices
For consumer-grade hardware, it is recommended to switch to the gpt-oss-20b model, whose 21B parameter enables real-time response on 16GB memory devices.
This answer comes from the articleCollection of scripts and tutorials for fine-tuning OpenAI GPT OSS modelsThe
May not be reproduced without permission:AI productivity tools " How to solve the problem of slow model inference in gpt-oss-recipes repository?
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