The GPT-OSS model series provides efficient deployment solutions for different application scenarios. gpt-oss-120b is suitable for datacenter or high-end device environments and runs on a single Nvidia H100 GPU, while gpt-oss-20b is optimized for low-latency scenarios and can run on a consumer device with only 16GB of RAM. The models support a variety of runtime frameworks, including Transformers, vLLM, Ollama, and LM Studio, to suit different hardware environments and usage requirements.
Particularly noteworthy is the use of MXFP4 quantization for this family of models, which greatly reduces runtime resource requirements, allowing large models to run efficiently on resource-limited devices. For Apple Silicon devices, developers can also convert the weights to Metal format for optimal local runtime performance. This flexible deployment strategy allows the GPT-OSS model to adapt to a wide range of hardware environments, from cloud servers to personal laptops.
This answer comes from the articleGPT-OSS: OpenAI's Open Source Big Model for Efficient ReasoningThe
