gpt-oss-120b
gpt-oss-120b is a 120-billion-parameter open-weight language model from OpenAI, released under Apache 2.0 license. It uses mixture-of-experts (MoE) architecture with only 5.1B active parameters and MXFP4 quantization to fit on a single 80GB GPU (H100/MI300X). Designed for reasoning-heavy tasks, agentic workflows, and production use cases. Requires the 'harmony' response format for correct operation.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | RedHatAI |
| Parameters | 120.4B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 40.7k |
| Likes | 5 |
| Last updated | 2026-05-18 |
| Source | RedHatAI/gpt-oss-120b |
What gpt-oss-120b is
120B-parameter MoE model with 5.1B active parameters. Post-trained with MXFP4 quantization of MoE weights. Trained on OpenAI's harmony response format (mandatory for inference). Supports configurable reasoning effort (low/medium/high), full chain-of-thought output, function calling, Python execution, and structured outputs. Validated on vLLM 0.10.1.1, Red Hat OpenShift AI 2.25, and Red Hat AI Inference Server 3.2.2. Context length unknown. Compatible with Transformers and PyTorch ecosystems.
Run gpt-oss-120b locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="RedHatAI/gpt-oss-120b")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Single 80GB GPU (NVIDIA H100, AMD MI300X recommended). VRAM estimate: ~80GB for full model with MXFP4 quantization of MoE weights and 8-bit precision on other parameters. Training/fine-tuning requirements not specified; inference-optimized specs provided only.
Model card states 'Fully customize models to your specific use case through parameter fine-tuning' but does not specify LoRA/QLoRA feasibility, training frameworks, or memory requirements for fine-tuning. Requires review of OpenAI's harmony repository or downstream documentation for adapter-based training details. Full parameter fine-tuning likely requires multi-GPU setup.
When to avoid it — and what to weigh
- Strict latency SLAs under 50ms per token — MoE activation and reasoning workloads introduce per-token latency overhead. Lower-latency use cases should consider gpt-oss-20b (21B parameters) or smaller dense models.
- Harmony format incompatible legacy systems — Model requires mandatory harmony response format application. Systems unable to modify prompt/response handling or using alternative chat templates may require custom integration work.
- Resource-constrained environments under 80GB VRAM — Even with quantization, full model requires ~80GB GPU memory. Environments limited to <16GB VRAM should use gpt-oss-20b or dense smaller models instead.
- Tasks requiring unknown context lengths — Model card does not specify maximum context window. Applications requiring specific sequence length guarantees should verify against OpenAI documentation or empirically test before deployment.
License & commercial use
Apache License 2.0 (permissive OSI license). No copyleft restrictions, no patent claims. Permits free use, modification, distribution, and commercial deployment without attribution requirement beyond license inclusion.
Apache 2.0 is an OSI-approved permissive license explicitly designed for commercial use without restrictions. Model card confirms 'Build freely without copyleft restrictions or patent risk—ideal for experimentation, customization, and commercial deployment.' Commercial use is clearly permitted. No gating, no evaluation requirements, no enterprise licensing needed.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Model card does not discuss security posture, red-teaming results, or safety measures. Full chain-of-thought output is 'not intended to be shown to end users'—indicates potential sensitive reasoning disclosure risk if exposed. Quantization (MXFP4) and sparsity may affect robustness behavior; requires evaluation before security-sensitive deployments. Requires review of OpenAI's safety documentation and arxiv paper (2508.10925) for threat modeling.
Alternatives to consider
gpt-oss-20b (OpenAI)
Smaller MoE variant (21B parameters, 3.6B active) for latency-sensitive or resource-constrained deployments. Fits in 16GB VRAM. Same license, harmony format, agentic capabilities. Trade-off: lower reasoning capacity.
Meta Llama 3.1 405B
Open-weight dense alternative (Llama 2 license non-OSI; gated). Larger capacity, different inference characteristics. No MoE sparsity; requires multi-GPU clusters. Strong community support and fine-tuning tools.
Mistral Large (7B/22B/123B)
Open-weight dense models (Mistral license; verify terms). Comparable or smaller scale. Better latency characteristics. Simpler fine-tuning ecosystem. No harmony format dependency.
Ship gpt-oss-120b with senior software developers
gpt-oss-120b fits a 120B-parameter reasoning engine on a single 80GB GPU without licensing overhead. Use the vLLM quickstart, OpenShift AI templates, or Transformers integration to spin up in minutes. Review the arxiv paper and OpenAI blog for safety and quantization details before production deployment.
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gpt-oss-120b FAQ
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Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If gpt-oss-120b is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to deploy advanced reasoning at scale?
gpt-oss-120b fits a 120B-parameter reasoning engine on a single 80GB GPU without licensing overhead. Use the vLLM quickstart, OpenShift AI templates, or Transformers integration to spin up in minutes. Review the arxiv paper and OpenAI blog for safety and quantization details before production deployment.