Model reference · open weights

gpt-oss-safeguard

LLMs openai Text gen 2 builds Open weights 79k dl/mo

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.

  • gpt-oss-safeguard-20b is a safety reasoning model developed by OpenAI for classifying text content based on user-provided safety policies.
  • The model features 21.5B parameters with 3.6B active parameters and is designed to fit within GPUs equipped with 16GB of VRAM.
  • It operates exclusively with the harmony response format and is distributed under the Apache 2.0 license.

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 byOpenAI
Released2025-09-18
Parameters21.5B
VRAM69.2 GB in vLLM, 65.2 GB of files for the weights

What it runs on

Memory and cards for gpt-oss-safeguard-120b (MXFP4)

69.2 GB in vLLM, 65.2 GB of filesweights, file size
37 MBcache per 1K tokens
156 MBwindow cache per request
706 MBruntime overhead, at least
131,072 tokenscontext max
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
———
A100 80 GB
FP4 without its speed-up here
186all 128K78.2 GB
H100 80 GB
FP4 without its speed-up here
6280K78.1 GB
RTX PRO 6000 Blackwell 96 GB3210all 128K93.8 GB
DGX Spark (GB10) 128 GB unified5418all 128K107 GB
H200 141 GB
FP4 without its speed-up here
10534all 128K138 GB
B200 180 GB16741all 128K176 GB
2× L40S 48 GB
tensor parallel · FP4 without its speed-up here
3712all 128K44.0 GB a card
4× RTX 4090 24 GB
tensor parallel · FP4 without its speed-up here
4615all 128K23.4 GB a card
4× RTX 3090 24 GB
tensor parallel · FP4 without its speed-up here
4715all 128K23.4 GB a card
4× RTX 5090 32 GB
tensor parallel
11338all 128K31.0 GB a card
2× H100 80 GB
tensor parallel · FP4 without its speed-up here
12642all 128K78.1 GB a card
2× A100 80 GB
tensor parallel · FP4 without its speed-up here
18762all 128K78.2 GB a card
Memory needed at each load
Requests at once8K tokens each32K tokens each
170.3 GB71.2 GB
572.2 GB76.7 GB
873.5 GB80.8 GB
1677.2 GB91.7 GB
3284.5 GB114 GB
6499.2 GB157 GB

One card, with vLLM's small-card settings.

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
gpt-oss-safeguard-20b ↗ 21.5BMXFP4 14.6 GB2× RTX 3060 12 GB
gpt-oss-safeguard-120b (above) ↗ 120.4BMXFP4 69.2 GBH100 80 GB

From the model card

What OpenAI says about gpt-oss-safeguard

Read 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.

Highlights

  • Trained to reason about safety : Trained and tuned for safety reasoning to accommodate use cases like LLM input-output filtering, online content labeling and offline labeling for Trust and Safety use cases.
  • Bring your own policy: Interprets your written policy, so it generalizes across products and use cases with minimal engineering.
  • Reasoned decisions, not just scores: Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in policy decisions. Keep in mind Raw CoT is meant for developers and safety practitioners. It’s not intended for exposure to general users or use cases outside of safety contexts.
  • Configurable reasoning effort: Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.
  • Permissive Apache 2.0 license: Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.

Inference examples

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.

Download the model

To download the model weights from Hugging Face hub using similar instructions to gpt-oss-120b.

Join the ROOST Model Community

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.

Resources

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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