GPT-OSS supports an extensive range of deployment options from consumer devices to enterprise cloud infrastructure. The models can be deployed through multiple inference backends including PyTorch for educational purposes, optimized Triton implementation with CUDA graphs and caching, Metal implementation for Apple Silicon, vLLM for production serving, and integration with Ollama and LM Studio for consumer hardware. Cloud providers Amazon, Baseten and Microsoft are making the models available on their platforms.
OpenAI provides reference implementations including terminal chat applications, a Responses API compatible server that implements browser tools and other Responses-compatible functionality, and client examples for various use cases. The Responses API server supports multiple inference backends including triton, metal for Apple Silicon, ollama, vllm, and transformers for local inference. Microsoft Azure AI Foundry provides unified platform support for building, fine-tuning, and deploying the models, while Foundry Local brings the models to edge devices for on-device inferencing.
The models integrate with development tools like Codex, which can be configured to work with any chat completions-API compatible server. Integration partners and platforms listed in the awesome-gpt-oss resource collection provide broader ecosystem support, while the models work with standard tools like transformers serve for OpenAI-compatible web servers. Both models support native tool use including web browsing through SimpleBrowserTool with configurable backends, Python code execution through Docker containers, and apply_patch functionality for file operations. The deployment flexibility extends from simple local installations using pip to sophisticated multi-GPU cloud deployments, making GPT-OSS suitable for everything from individual developer experimentation to enterprise-scale production applications.
For more detailed information, see: GPT-oss vs o4-mini: Edge-Ready, On-Par Performance — Dependable, Not Mind-Blowing