Opening article
OpenAI has released GPT OSS, an open‑weights model family comprising a 120B and a 20B parameter model. Both are mixture‑of‑experts and use 4‑bit MXFP4 quantization, allowing the 20B model to run on 16GB GPUs and the 120B model to fit on a single H100. The models are licensed Apache 2.0 and available through Hugging Face’s Inference Providers, Azure AI Model Catalog, and Dell Enterprise Hub.
GPT OSS is a hugely anticipated open-weights release by OpenAI, designed for powerful reasoning, agentic tasks, and versatile developer use cases. It comprises two models: a big one with 117B parameters ( gpt-oss-120b ), and a smaller one with 21B parameters ( gpt-oss-20b ). Both are mixture-of-experts (MoEs) and use a 4-bit quantization scheme (MXFP4), enabling fast inference (thanks to fewer active parameters, see details below) while keeping resource usage low. The large model fits on a single H100 GPU, while the small one runs within 16GB of memory and is perfect for consumer hardware and on-device applications.
To make it even better and more impactful for the community, the models are licensed under the Apache 2.0 license , along with a minimal usage policy:
We aim for our tools to be used safely, responsibly, and democratically, while maximizing your control over how you use them. By using gpt-oss, you agree to comply with all applicable law.
The model weights are quantized in mxfp4 format, which was originally available on GPUs of the Hopper or Blackwell families, but now works on previous CUDA architectures (including Ada, Ampere, and Tesla). Installing triton 3.4, together with the kernels library, makes it possible to download optimized mxfp4 kernels on first use, achieving large memory savings. With these components in place, you can run the 20B model on GPUs with 16 GB of RAM. This includes many consumer cards (3090, 4090, 5080) as well as Colab and Kaggle!
If the previous libraries are not installed (or you don’t have a compatible GPU), loading the model will fall back to bfloat16 , unpacked from the quantized weights.
GPT OSS supports two kinds of tools: The “builtin” tools browser and python , and custom tools supplied by the user. To enable builtin tools, pass their names in a list to the builtin_tools argument of the chat template, as shown below. To pass custom tools, you can pass them either as JSON schema or as Python functions with type hints and docstrings using the tools argument. See the chat template tools documentation for more details, or you can just modify the example below:
According to OpenAI, this release is a meaningful step in their commitment to the open-source ecosystem, in line with their stated mission to make the benefits of AI broadly accessible. Many use cases rely on private and/or local deployments, and we at Hugging Face are super excited to welcome OpenAI to the community. We believe these will be long-lived, inspiring and impactful models.
You need to install the latest transformers release (v4.55.1 or later), as well as accelerate and kernels . We also recommend installing triton 3.4 or better, as it unblocks support for mxfp4 quantization on CUDA hardware:
OpenAI GPT OSS has been verified on AMD Instinct hardware, and we’re happy to announce initial support for AMD’s ROCm platform in our kernels library, setting the stage for upcoming optimized ROCm kernels in Transformers. MegaBlocks MoE kernel acceleration is already available for OpenAI GPT OSS on AMD Instinct (e.g., MI300-series), enabling better training and inference performance. You can test it with the same inference code shown above.
Llama.cpp offers native MXFP4 support with Flash Attention, delivering optimal performance across various backends such as Metal, CUDA, and Vulkan, right from the day-0 release.
OpenAI GPT OSS models are accessible through Hugging Face’s Inference Providers service, allowing you to send requests to any supported provider using the same JavaScript or Python code. This is the same infrastructure that powers OpenAI’s official demo on gpt-oss.com , and you can use it for your own projects.
Below is an example that uses Python and the super-fast Cerebras provider. For more info and additional snippets, check the inference providers section in the model cards and the dedicated guide we crafted for these models .
Inference Providers also implements an OpenAI-compatible Responses API, the most advanced OpenAI interface for chat models, designed for more flexible and intuitive interactions. Below is an example using the Responses API with the Fireworks AI provider. For more details, check out the open-source responses.js project.
The release expands open‑source access to large‑scale reasoning models, enabling broader experimentation and deployment across diverse hardware ecosystems.