Model reference · open weights
gpt-oss-safeguard is an open-weight language model from OpenAI. gpt-oss-safeguard-120b (MXFP4) weighs 69.2 GB in vLLM, 65.2 GB of files; the smallest configuration that runs it is H100 80 GB.
Summary of the openai/gpt-oss-safeguard-20b model card, 2026-10-01. The estimate below is for another build of the family.
What it is
| Released by | OpenAI |
|---|---|
| Released | 2025-09-18 |
| Parameters | 21.5B |
| VRAM | 69.2 GB in vLLM, 65.2 GB of files for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB … L40S 48 GB 6 smaller cards | — | — | — | |
| A100 80 GB FP4 without its speed-up here | 18 | 6 | all 128K | 78.2 GB |
| H100 80 GB FP4 without its speed-up here | 6 | 2 | 80K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 32 | 10 | all 128K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 54 | 18 | all 128K | 107 GB |
| H200 141 GB FP4 without its speed-up here | 105 | 34 | all 128K | 138 GB |
| B200 180 GB | 167 | 41 | all 128K | 176 GB |
| 2× L40S 48 GB tensor parallel · FP4 without its speed-up here | 37 | 12 | all 128K | 44.0 GB a card |
| 4× RTX 4090 24 GB tensor parallel · FP4 without its speed-up here | 46 | 15 | all 128K | 23.4 GB a card |
| 4× RTX 3090 24 GB tensor parallel · FP4 without its speed-up here | 47 | 15 | all 128K | 23.4 GB a card |
| 4× RTX 5090 32 GB tensor parallel | 113 | 38 | all 128K | 31.0 GB a card |
| 2× H100 80 GB tensor parallel · FP4 without its speed-up here | 126 | 42 | all 128K | 78.1 GB a card |
| 2× A100 80 GB tensor parallel · FP4 without its speed-up here | 187 | 62 | all 128K | 78.2 GB a card |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 70.3 GB | 71.2 GB |
| 5 | 72.2 GB | 76.7 GB |
| 8 | 73.5 GB | 80.8 GB |
| 16 | 77.2 GB | 91.7 GB |
| 32 | 84.5 GB | 114 GB |
| 64 | 99.2 GB | 157 GB |
One card, with vLLM's small-card settings.
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| gpt-oss-safeguard-20b ↗ | 21.5B | MXFP4 | 14.6 GB | 2× RTX 3060 12 GB |
| gpt-oss-safeguard-120b (above) ↗ | 120.4B | MXFP4 | 69.2 GB | H100 80 GB |
From the model card
gpt-oss-safeguard-120b and gpt-oss-safeguard-20b are safety reasoning models built-upon gpt-oss. With these models, you can classify text content based on safety policies that you provide and perform a suite of foundational safety tasks. These models are intended for safety use cases. For other applications, we recommend using gpt-oss models.
This model gpt-oss-safeguard-20b (21B parameters with 3.6B active parameters) fits into GPUs with 16GB of VRAM. Check out gpt-oss-safeguard-120b (117B parameters with 5.1B active parameters) for the larger model.
Both models were trained on our harmony response format and should only be used with the harmony format as it will not work correctly otherwise.
You can use gpt-oss-safeguard-120b and gpt-oss-safeguard-20b similar to gpt-oss-120b and gpt-oss-20b as described in our respective cookbooks. We’ve also provided a detailed prompting guide that provides guidelines for how to craft your policy and use it with the models.
To download the model weights from Hugging Face hub using similar instructions to gpt-oss-120b.
gpt-oss-safeguard is a model partner of the Robust Open Online Safety Tools (ROOST) Model Community. The ROOST Model Community (RMC) is a group of safety practitioners exploring open source AI models to protect online spaces. As an RMC model partner, OpenAI is committed to incorporating user feedback and jointly iterating on future releases in pursuit of open safety. Visit the RMC GitHub repo to learn more about this partnership and how to get involved.
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.