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gpt-oss-safeguard-20b: Self-Hosting & Deployment Guide

gpt-oss-safeguard-20b is an OpenAI model name that appears to reference a 20-billion-parameter, safety-focused open-source-style GPT variant, but OpenAI has not publicly released authoritative technical details about it.

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What is gpt-oss-safeguard-20b?

gpt-oss-safeguard-20b is a named OpenAI model that suggests a 20B-parameter GPT focused on open-source alignment or safety, but it is not formally documented by OpenAI. In practice, such a model name might be used in experimental or internal contexts for research, prototyping, or safety tooling, but no canonical public description exists. Without official documentation, its concrete production use cases, benchmarks, and deployment patterns are unknown. It is presumably related in spirit to the broader GPT family of large language models from OpenAI, but cannot be placed confidently within a specific, publicly described model lineage.

How to run gpt-oss-safeguard-20b yourself

Open weights (apache-2.0), 21.5B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.

gpt-oss-safeguard-20b VRAM requirements

Precision / quantizationWeightsFigureSource
BF16 (full precision)43 GBEstimate21.5B params × 2 bytes
GGUF Q4_K_M11.6 GBFile sizeunsloth/gpt-oss-safeguard-20b-GGUF

File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.

GPU requirements: deploy gpt-oss-safeguard-20b on your hardware

HardwareBF16 (full precision)GGUF Q4_K_M
NVIDIA H200141 GB1×1×
NVIDIA H10080 GB1×1×
NVIDIA A100 80GB80 GB1×1×
NVIDIA L40S48 GB2×1×

GPUs needed per variant.

HardwareGGUF Q4_K_M
RTX 509032 GB1×
RTX 409024 GB1×
RTX 309024 GB1×

GGUF via llama.cpp; more cards or CPU offload for larger files.

HardwareGGUF Q4_K_M
Mac, 512 GBM3 UltraFits
Mac, 192 GBM2 UltraFits
Mac, 128 GBM4 Max / M3 MaxFits
Mac, 64 GBM4 Pro / MaxFits

Assumes ~75% of unified memory is usable by the GPU.

HardwareBF16 (full precision)GGUF Q4_K_M
AWS p5.48xlarge8× H100 · 640 GBFitsFits
AWS p4de.24xlarge8× A100 80GB · 640 GBFitsFits
AWS g6e.12xlarge4× L40S · 192 GBFitsFits
Google Cloud a3-highgpu-8g8× H100 · 640 GBFitsFits
Azure ND H100 v58× H100 · 640 GBFitsFits

Whole-instance GPU memory; public instance specs.

Serve it yourself

pip install -U vllm
vllm serve openai/gpt-oss-safeguard-20b
pip install 'sglang[all]'
python -m sglang.launch_server --model-path openai/gpt-oss-safeguard-20b
llama-server -hf unsloth/gpt-oss-safeguard-20b-GGUF:Q4_K_M \
  --ctx-size 32768 --n-gpu-layers 99

Commands use the official repositories above; vLLM and SGLang expose an OpenAI-compatible endpoint.

Not on LLM.API yet

We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.

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Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput
Groq$0.0001 in
$0.0003 out

Provider list prices; the LLM.API discount applies on top.

Provider Input Output Cache read /M Latency Throughput Uptime
Groqin $0.0001; out $0.0003 per 1M tokens131K tokenstools, streaming, reasoning, JSON, structured

List prices from third-party hosts in the OpenRouter public catalogue (openai/gpt-oss-safeguard-20b); LLM API does not serve this model and applies no discount. Updated weekly. Last updated 4 Oct 2026.

Try this model

Test gpt-oss-safeguard-20b right here — free to start.

gpt-oss-safeguard-20b
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Not available on LLM API yet

We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.

Browse available models

Uptime

30-Day Uptime
96.70%
Past Incidents (30d)
3
Error rate (24h)
9.27%

Last 30 days

26/30 days operational | 96.70% uptime

30 days ago Today
Operational Degraded Outage Maintenance
See All Incidents

gpt-oss-safeguard-20b: Self-Hosting & Deployment Guide in production

What the model is good at.

  • Conversational AI

    Engages in multi-turn, context-aware conversations, following instructions and maintaining coherent dialogue across diverse general-purpose topics.

  • Text Translation

    Translates written content between multiple languages while preserving meaning and tone, supporting multilingual understanding and communication.

  • Content Moderation

    Supports detection of sensitive or harmful text content to help implement safety policies and reduce inappropriate or unsafe outputs.

  • Visual Reasoning

    Interprets and reasons about images, connecting visual details with textual instructions to answer questions or provide descriptions.

  • Text Extraction

    Reads and extracts textual information from images or documents, enabling downstream analysis, search, or transformation of the captured text.

What you get when you call gpt-oss-safeguard-20b: Self-Hosting & Deployment Guide through LLM.API.

  • Unified AI Routing

    Automatically route each request to the optimal model across providers based on performance, latency, and cost—without changing your application code or client libraries.

    One endpoint, every model

  • Cost-Aware Orchestration

    Control spend with smart model selection, budgets, and policies that downshift to cheaper options when quality allows—so you scale usage without surprise invoices.

    Max performance, minimal spend

  • Resilient Fallback Logic

    Define automatic failover chains so timeouts, rate limits, or provider outages transparently roll to backup models—keeping your AI features online under real-world traffic.

    Never ship single-provider

  • End-to-End Observability

    Get query-level traces, latency, cost, and error analytics across all providers in one place—so you can debug incidents and tune routing with real production data.

    See every token, everywhere

  • Task-Level Abstractions

    Call high-level tasks like chat, RAG, or tools instead of raw models, letting LLM.API handle prompts, parameters, and provider quirks behind a stable interface.

    Code to tasks, not models

  • High-Throughput Batch Jobs

    Run large-scale embeddings, classification, and content generation as efficient batch jobs with concurrency controls and retries—optimized to squeeze more work per dollar.

    Bulk workloads, single call

When to Use — When NOT to Use

Use it if...

  • You need a guardrail model to classify and filter unsafe user-generated content.
  • You need automated moderation of prompts and responses before passing them to larger models.
  • Your use case involves batch-scoring large text corpora for safety or policy compliance.
  • You need structured safety labels or risk scores to feed downstream business logic.
  • Your use case involves building a safety gateway in front of multiple LLM providers.
  • You need a dedicated safety model to separate moderation concerns from application logic.

Avoid if...

  • You need a general-purpose chat or reasoning model rather than a safety specialist.
  • Your workload requires high-quality code generation, debugging help, or complex software design.
  • You need creative writing, content generation, or brainstorming beyond classification-style outputs.
  • Your workload requires detailed domain reasoning, such as finance, law, or advanced science.
  • You need multimodal understanding or generation, including images, audio, or video handling.
  • Your workload requires tool use, function calling, or orchestrating multi-step agent workflows.

COMMUNITY

What developers say about gpt-oss-safeguard-20b

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

  • Developers report that GPT-OSS-Safeguard-20b displays measurably different values than its base model, often producing more neutral or indecisive ratings when evaluating controversial opinions on a Likert scale.
  • One developer noted confusing discrepancies in model behavior when comparing outputs from the Hugging Face Inference API (via Groq) against local execution on a GPU.
  • Safety practitioners value the model for making production-level safety checks economically viable, highlighting its ability to interpret custom natural language policies and provide transparent chain-of-thought reasoning.

SOURCES

Frequently Asked Questions

  • What is gpt-oss-safeguard-20b?

    gpt-oss-safeguard-20b is a 20-billion-parameter OpenAI model focused on safe, instruction-following text generation for general-purpose applications.

  • What is gpt-oss-safeguard-20b best suited for?

    It is best for building safety-conscious chatbots, assistants, and content pipelines that require strong refusal behavior and policy-aligned generations.

  • What context window does gpt-oss-safeguard-20b support?

    gpt-oss-safeguard-20b supports up to a 32,000-token context window for combined input and output.

  • What modalities does gpt-oss-safeguard-20b support?

    This model supports text input and text output only; it does not process images, audio, or video.

  • How fast is gpt-oss-safeguard-20b when called through LLM.API?

    Typical end-to-end latency is in the low-seconds range, depending on prompt length, output length, and your selected LLM.API region.

  • How is gpt-oss-safeguard-20b priced on LLM.API?

    Pricing is usage-based per input and output token, with exact rates shown in your LLM.API dashboard and billing documentation.

  • How do I call gpt-oss-safeguard-20b via the LLM.API?

    Set the model field to "gpt-oss-safeguard-20b" in your LLM.API completion or chat endpoint request and provide your LLM.API key.

  • How does gpt-oss-safeguard-20b compare to similar 20B models?

    Compared to generic 20B open-source models, it emphasizes stronger safety alignment and refusals, sometimes trading off creativity or permissiveness.

  • Does gpt-oss-safeguard-20b support streaming responses over LLM.API?

    Yes, you can enable token streaming by setting the appropriate streaming flag in your LLM.API request.

  • What are the main limitations of gpt-oss-safeguard-20b?

    It may refuse borderline content, occasionally over-censor benign requests, hallucinate facts, and lacks image, audio, or tool-native capabilities.

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