OpenAI launches open-weight models GPT OSS 120b & GOT OSS 20b: Check how to get new models on your computer

OpenAI has launched two open-weight AI reasoning models, gpt-oss-120b and gpt-oss-20b, for public use, marking its first open release since GPT-2 in 2019. The move comes amid growing pressure from global developers and rising competition from China’s DeepSeek and Meta’s Llama, which have embraced open-source approaches.

What are open-weight models and how are they different?

The released gpt-oss models are classified as ‘open-weight’ — not fully open-source. While open-source models provide full access to source code, architecture, training data, and allow modifications, open-weight models only provide the trained weights. This means users can run and adapt the model, but cannot see or change its training details or code.

This distinction affects how much transparency and customisation developers can expect. OpenAI’s decision to release open-weight, instead of open-source, reflects a compromise between openness and maintaining control over the core technology.

How to get and use OpenAI’s new gpt-oss models

Developers can access OpenAI’s gpt-oss-120b and gpt-oss-20b models by downloading them from Hugging Face. The gpt-oss models can be run on personal laptops or single Nvidia GPUs, offering developers the ability to deploy them locally or through Microsoft’s Azure cloud platform. This is a major shift for OpenAI, which has primarily focused on proprietary AI models since the release of GPT-3.

These models are also integrated into Microsoft’s Azure AI Foundry platform, where users can deploy them with a few command-line instructions. For those preferring offline or local deployment, the gpt-oss-20b model is available via Foundry Local on Windows devices and will soon be supported on MacOS. Developers can follow the QuickStart guides provided by Microsoft to run the models, fine-tune them using their own data, or deploy them across cloud and edge environments depending on their performance and privacy needs.

Why OpenAI changed its strategy

The shift in OpenAI’s approach comes after criticism and increased competition in the AI space. China’s DeepSeek, an open-source language model, gained global attention for offering similar capabilities at significantly lower development costs.

Meta’s Llama models also followed the open-weight route and have crossed over a billion downloads, although some developers have raised concerns over their licensing terms.

Earlier this year, OpenAI CEO Sam Altman acknowledged the need for a new approach. In a Reddit Q&A, Altman said, “[I personally think we need to] figure out a different open source strategy. Not everyone at OpenAI shares this view, and it’s also not our current highest priority… We will produce better models, but we will maintain less of a lead than we did in previous years.”

Technical details and deployment options

The gpt-oss-120b model is designed for complex reasoning tasks such as code generation, math problems, and domain-specific queries. It contains 120 billion parameters and can run on a single enterprise-level GPU. The gpt-oss-20b model is lightweight and can run on consumer laptops with 16GB of memory. It is suited for local inference tasks like code execution and autonomous workflows.

Both models are supported by Microsoft’s Azure AI Foundry platform, which allows developers to fine-tune, adapt, and deploy them on both cloud and local systems. The gpt-oss-20b model will also be supported on Windows and soon on MacOS.

Microsoft’s role and wider platform integration

Microsoft, OpenAI’s key partner, announced the integration of the gpt-oss models into its Azure AI Foundry and Windows AI Foundry platforms. According to Microsoft, this enables developers to run these models securely and efficiently across cloud, edge, and offline environments.

“With the launch of OpenAI’s gpt-oss models—its first open-weight release since GPT-2—we’re giving developers and enterprises unprecedented ability to run, adapt, and deploy OpenAI models entirely on their own terms,” Microsoft said.

What developers and businesses can expect

For developers, having access to open weights means greater transparency and the ability to customise models for specific use cases. They can fine-tune models using techniques like LoRA and PEFT, integrate their own data, and deploy across different environments.

For businesses, this change provides flexibility and control. Without relying solely on cloud-based tools or black-box APIs, they can now host and deploy AI applications with greater security, data sovereignty, and cost control.

Next steps

The gpt-oss models are currently available via Azure AI Foundry. Developers can deploy them using command-line tools, fine-tune them with proprietary data, or integrate them into hybrid cloud and edge environments. Foundry Local supports gpt-oss-20b for on-device inference across CPUs, GPUs, and NPUs.

OpenAI’s new direction is part of a broader trend in the AI industry towards balancing openness with responsible deployment, while supporting innovation across both proprietary and open ecosystems.