From the model card
Fields below are copied from the tags and counters on the HuggingFace repository openai/gpt-oss-20b at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.
- Publisher (HF namespace)
- openai
- Pipeline tag
- text-generation
- Library
- Transformers, vLLM
- Weight formats
- safetensors
- License tag
apache-2.0— read the license file in the repo before relying on it- Papers cited
- arXiv:2508.10925
- Downloads (HF counter at last fetch)
- 6,206,840
- Likes (HF counter at last fetch)
- 4,987
- Model card
- https://huggingface.co/openai/gpt-oss-20b
Use cases
- High-quality open-weight text generation for enterprise applications
- Research into OpenAI's architectural choices at open-weight scale
- Self-hosted LLM deployment where API cost or privacy is a concern
- Benchmarking against proprietary API models for cost-quality tradeoffs
- Quantized deployment via vllm for efficient batched serving
Pros
- Apache 2.0 license — OpenAI's first major open-weight commercial release
- 20B scale provides strong generation quality
- vllm-compatible for efficient production serving
- FP8 and MXfp4 quantization for reduced VRAM requirements
Cons
- 20B parameters require substantial GPU infrastructure for full-precision inference
- Knowledge cutoff and training data scope not fully documented at publication time
- Community fine-tunes and adapters are nascent given recent release
- FP8 inference requires hardware supporting float8 (Hopper+ GPUs)
- Benchmark comparisons against frontier models not yet fully established
Tags
transformerssafetensorsgpt_osstext-generationvllmconversationalarxiv:2508.10925license:apache-2.0eval-resultsendpoints_compatible8-bitmxfp4deploy:sagemakerdeploy:azureregion:us