gpt-oss · OpenAI
Fine-tuning gpt-oss 120B
What does it take to fine-tune gpt-oss 120B?
gpt-oss 120B needs 220 GB for half-precision LoRA training and 80 GB in four-bit, and 80 GB to serve. It supports supervised fine-tuning, under the Apache 2.0 licence. The cheapest qualifying class is A100 80 GB at $1.68 per GPU-hour.
A hundred and seventeen billion parameters with 5.1 billion active. A single 80GB GPU will serve it; full half-precision fine-tuning needs far more, so the practical route is a four-bit run at 80GB. The largest Apache-2.0 model in the catalogue.
What to know before choosing it
A hundred and seventeen billion parameters that serve on a single 80GB class is the most striking number in the catalogue, and it is entirely a consequence of only 5.1 billion being active per token.
Full half-precision fine-tuning needs far more than any single class provides, so the practical route is a four-bit run at 80GB. It is also the largest Apache-2.0 model here, which for some organisations is the deciding factor on its own.
Specification
| Parameters | 116.8B5.1B active per token |
|---|---|
| Architecture | Mixture of experts |
| Size tier | Extra large |
| LoRA training memory | 220 GBHalf precision, frozen base |
| QLoRA training memory | 80 GBFour-bit base, higher-precision adapter |
| Serving memory | 80 GBHalf precision, before attention cache |
| Objectives | SFT |
| Licence | Apache 2.0 |
| Repository | openai/gpt-oss-120b |
What it costs to train
Every class with enough memory for four-bit training, cheapest first. Total cost is the rate multiplied by wall time, so the cheapest rate is not always the cheapest run — a faster class that finishes sooner frequently wins.
| GPU class | VRAM | Training / hr | Serving / hr | Fits |
|---|---|---|---|---|
| A100 80 GB | 80 GB | $1.68 | $1.94 | QLoRA only |
| H100 80 GB | 80 GB | $2.59 | $2.99 | QLoRA only |
| H200 | 141 GB | $4.55 | $5.25 | QLoRA only |
Serving fits on A100 80 GB at $1.94 per hour — before the attention cache, which grows with context length and concurrency.
Good starting point for
- Frontier-scale capability under a permissive licence
- Serving on a single 80GB class
This is a mixture-of-experts model
116.8B total parameters with 5.1B active per token. Memory scales with the total; inference compute scales with the active count. That makes it attractive when memory is cheaper than compute for your workload, and unattractive when the reverse holds. Quoting only one of the two numbers is how these models get misrepresented in both directions.
Other sizes in this family
| Model | Params | QLoRA | Objectives |
|---|---|---|---|
| gpt-oss 20B | 20.9B | 24 GB | SFT |
Comparable sizes elsewhere
Frequently asked questions
How much VRAM does it take to fine-tune gpt-oss 120B?
220 GB for half-precision LoRA and 80 GB for four-bit QLoRA. Serving needs 80 GB. Preference tuning roughly doubles the training figure, because a frozen reference model is held alongside the one being trained.
What is the cheapest way to fine-tune gpt-oss 120B?
Four-bit QLoRA on A100 80 GB at $1.68 per GPU-hour is the cheapest class that meets the 80 GB threshold. Note that quantised training is slower per step, so a faster class sometimes costs less over the whole run.
Can I download the weights after fine-tuning gpt-oss 120B?
Yes. Every finished run exposes its trained weights for download, and publishing to a model hub is a single call with a generated model card recording the base model and version the adapter applies to.
What licence does gpt-oss 120B carry?
Apache 2.0. The licence follows the fine-tune — a derivative inherits the base model’s terms, and those terms pass to anyone you give the model to.
Last verified 6 August 2026. Memory thresholds are the platform's own admission limits.
Fine-tune gpt-oss 120B
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