GPT-OSS 120B

Who should use this model?

• Server-side local reasoning tests, agents, and teams with 60GB-plus memory available for experimentation and deployment.

Choose something else when

• Avoid if you lack workstation-class hardware or want a simple beginner laptop model to run.

What hardware makes sense?

The download is only part of the memory needed. Leave room for the app, the input it processes, and other open programs. For chat models, context includes the instructions, conversation, and answer. Longer context needs more memory.

CPU only

Use a smaller model for this memory limit.

8GB system memory

Use a smaller model for this memory limit.

16GB system memory

Use a smaller model for this memory limit.

32GB system memory

Use a smaller model for this memory limit.

8GB GPU memory

Use a smaller model for this memory limit.

12GB GPU memory

Use a smaller model for this memory limit.

24GB GPU memory

Use a smaller model for this memory limit.

Apple unified memory

Use a smaller model for this memory limit.

Model facts that change the decision

Parameters

117B total / 5.1B active

Input

Text

Published context limit

131,072 tokens

Model format

safetensors · GGUF · Ollama · MLX

Suggested compression

Q4_K_M for first local attempts unless a cited runtime recommends a different default.

Typical Q4 file

62.84GB before runtime and context overhead

Languages

Unknown

Conditions that change the answer

• Consumer-hostile for local beginners; realistic local use requires workstation, server, or very high-memory unified-memory hardware.
• Use workstation or server hardware for this model.
• OpenAI positions it as fitting a single 80GB GPU; local guides describe it as best with 60GB-plus VRAM or unified memory.
• The common Ollama and LM Studio packages are about 65GB, before considering runtime overhead.
• Tool use depends on the app or server exposing tools; the model alone does not provide web browsing, Python, or function execution.
• Higher reasoning effort can be useful but will materially affect latency.
• The 128k context window is an upper limit; full-context use can be slow and memory-heavy.
• Common third-party quants are not much smaller than MXFP4; Q5 size was not specified here in the checked 120B GGUF source.
• Production use needs separate safeguards because open weights can be modified, fine-tuned, or deployed without OpenAI-side mitigations.
• Commercial use: permitted, subject to OpenAI gpt-oss usage policy
• GPT-OSS 120B is the large open-weight reasoning model, aimed at serious reasoning, tools, and production experiments. It is not beginner-friendly locally because realistic use requires server, workstation, or high-memory unified hardware rather than consumer laptops.