gpt-oss-120b-GGUF
gpt-oss-120b is OpenAI's open-weight 120B parameter model (5.1B active via MoE) released under Apache 2.0. It is a GGUF-quantized version maintained by Unsloth, designed for reasoning-heavy tasks, agentic workflows, and general-purpose production use. The model uses native MXFP4 quantization in its MoE layer and requires the Harmony response format. It fits on a single H100 GPU and supports fine-tuning, function calling, and tool use.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | unsloth |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 168.1k |
| Likes | 278 |
| Last updated | 2025-08-25 |
| Source | unsloth/gpt-oss-120b-GGUF |
What gpt-oss-120b-GGUF is
gpt-oss-120b is a Mixture-of-Experts language model trained on the Harmony response format. The base model is 120B parameters with 5.1B active parameters. This GGUF variant is quantized by Unsloth and includes optimizations beyond the original OpenAI release. It supports inference via Transformers, vLLM (with custom wheels), Ollama, and llama.cpp. The model requires manual Harmony format application when using model.generate() directly but is integrated into Transformers' chat template. Context length is unknown. Fine-tuning is stated as feasible on a single H100 node for the 120B variant.
Run gpt-oss-120b-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="unsloth/gpt-oss-120b-GGUF")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
**Estimate to verify:** 120B parameters with MXFP4 quantization in MoE layer fits on single H100 (80GB); full precision inference not practical on consumer hardware. Smaller gpt-oss-20b (21B parameters) can run within 16GB. Exact VRAM footprint depends on quantization level chosen (GGUF format supports multiple quantization schemes) and framework overhead. For CPU inference, llama.cpp via Ollama supports smaller quantizations on 32GB+ systems; production serving on GPU recommended.
Model card states full parameter fine-tuning is feasible on a single H100 node for 120B variant. LoRA/QLoRA support not explicitly mentioned but is standard in Transformers ecosystem. No guidance on optimal learning rates, batch sizes, or Harmony format preservation during fine-tuning provided. Practitioners should reference OpenAI's gpt-oss documentation and community examples.
When to avoid it — and what to weigh
- You require guaranteed context window size — Context length is not disclosed in the model card. You must verify this against your application requirements before deployment.
- You need inference on consumer hardware without quantization — 120B parameters in full precision exceeds typical consumer VRAM. Ollama and LM Studio offer alternatives, but only with quantization or smaller variants (gpt-oss-20b).
- You cannot enforce Harmony response format in your pipeline — The model is trained exclusively on Harmony format and will not function correctly without it. This adds a mandatory dependency to your inference stack.
- You require stable, out-of-the-box compatibility with all LLM frameworks — vLLM requires a custom wheel build from Unsloth's index; standard vLLM may not work. Integration maturity varies across frameworks.
License & commercial use
Apache License 2.0 (OSI-compliant, permissive). No copyleft restrictions, patent protection, or field-of-use clauses. License is clear and does not impose derivative work obligations.
Apache 2.0 is a permissive OSI license that explicitly permits commercial use, modification, and distribution. No patent risk or additional licensing required for commercial deployment. Model weights are open and redistributable. However, practitioners should verify compliance with any underlying training data licensing, OpenAI's terms of service for derivative works, and cloud provider terms if hosting commercially.
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 |
No explicit security audit, adversarial robustness, or vulnerability disclosure details provided in the model card. As an open-weight model, the weights themselves are inspectable but the training process and safety measures are not fully transparent. Inference via external services (e.g., vLLM endpoints) introduces network/access control considerations. Prompt injection and hallucination risks are not addressed. Users should implement standard LLM safety practices: input validation, output filtering, rate limiting, and audit logging for production agentic workloads.
Alternatives to consider
gpt-oss-20b (OpenAI, same org)
Smaller variant (21B parameters, 3.6B active) with lower latency and consumer-hardware compatibility (16GB RAM). Trade-off: less reasoning capacity than 120B.
Meta Llama 3.1 (405B or 70B)
Also Apache 2.0; larger reasoning capacity and broader fine-tuning community. Trade-off: no native MoE optimization; requires more hardware for 405B variant.
Mistral Large or Mistral 7B
Apache 2.0 licensed; strong reasoning and agentic capabilities at different scales. Trade-off: different training approach; less emphasis on full chain-of-thought transparency.
Ship gpt-oss-120b-GGUF with senior software developers
Start with Unsloth's GGUF quantization for immediate inference, or fine-tune on your H100 for domain-specific reasoning. Verify Harmony format integration and context length for your use case. Contact Devco for production deployment architecture review.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
gpt-oss-120b-GGUF FAQ
Can I use gpt-oss-120b commercially without additional licensing?
What GPU do I need to run gpt-oss-120b?
Do I have to use the Harmony format?
Is the model actively maintained?
Custom software development services
Need help beyond evaluating gpt-oss-120b-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to Deploy gpt-oss-120b?
Start with Unsloth's GGUF quantization for immediate inference, or fine-tune on your H100 for domain-specific reasoning. Verify Harmony format integration and context length for your use case. Contact Devco for production deployment architecture review.