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# GPT-OSS Models

<a id="model-catalog-gpt-oss" />

This page provides detailed technical specifications for the OpenAI GPT-OSS model family supported by NeMo Customizer. For supported features and capabilities, refer to [Tested Models](/documentation/customizer-reference/models/model-catalog).

## Before You Start

These models require a Hugging Face token to download. Create a secret with your Hugging Face API key, then create a FileSet and Model Entity referencing the model. See [index](/documentation/customizer-reference/manage-model-entities/overview) for setup instructions.

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## GPT-OSS 20B

| Property                | Value                                                                                                                                      |
| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| Creator                 | OpenAI                                                                                                                                     |
| Architecture            | Mixture of Experts (MoE) Transformer                                                                                                       |
| Description             | GPT-OSS 20B provides lower latency for local or specialized use cases, featuring full chain-of-thought reasoning and agentic capabilities. |
| Context Length          | 131,072 tokens                                                                                                                             |
| Parameters              | 21B parameters (3.6B active parameters)                                                                                                    |
| Training Data           | Trained on harmony response format                                                                                                         |
| Checkpoint Quantization | MXFP4 quantization of the MoE weights                                                                                                      |
| Inference Memory        | The official checkpoint can run within 16 GB of memory; Customizer training requires the GPU configurations below                          |
| Default Name            | openai/gpt-oss-20b                                                                                                                         |
| Hugging Face            | [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)                                                                            |

### Training Options (20B)

The Automodel contract matrix validates these configurations:

| Fine-Tuning | Dataset Format            | GPUs | Sequence Packing                           |
| ----------- | ------------------------- | ---- | ------------------------------------------ |
| LoRA        | Prompt-completion         | 1    | Supported                                  |
| Full SFT    | Prompt-completion or chat | 8    | Not supported in the tested configurations |

For the tested Full SFT configuration, tensor and pipeline parallel sizes are `1` and expert parallel size is `8`. The tested LoRA configuration uses one GPU without expert parallelism.

Default training max sequence length: 4096.

### Deployment Configuration

* **LoRA**:
* NIM Image: `nvcr.io/nim/openai/gpt-oss-20b:1.6.1-variant`
* GPU Count: 2x 80GB
* Additional Environment Variables:
* `NIM_DISABLE_MODEL_DOWNLOAD`: `1`
* `NIM_WORKSPACE`: `/model-store`
* **Full SFT**:
* NIM Image: `nvcr.io/nim/nvidia/llm-nim:1.15.5`
* GPU Count: 2x 80GB
* Additional Environment Variables:
* `NIM_MODEL_PROFILE`: `vllm`

## Usage Recommendations

### Reasoning Levels

GPT-OSS models support configurable reasoning levels that you can set in system prompts:

* **Low**: Fast responses for general dialogue
* **Medium**: Balanced speed and detail
* **High**: Deep and detailed analysis

Example: Set reasoning level using "Reasoning: high" in the system prompt.

### MoE Model Parallelization

GPT-OSS models use a Mixture of Experts (MoE) architecture and benefit from specialized parallelization across expert layers for optimal performance.

**MoE Parallelism Constraints**

MoE models only support expert parallelism for distributing experts across GPUs. When `expert_parallel_size > 1`, `tensor_parallel_size` must be set to 1. Additionally, `expert_parallel_size` must evenly divide the number of GPUs. These constraints apply to training parallelism only and NIM deployment may use different GPU counts optimized for inference.

### Model Selection Guidelines

* **GPT-OSS 20B**: Ideal for lower latency requirements, local deployment, specialized use cases, and consumer hardware (with Ollama support)

### Important Usage Note

GPT-OSS models use the harmony response format and require this format for proper functionality.

For GPT-OSS 20B, sequence packing is supported by the tested Automodel LoRA configuration with prompt-completion data. It is not supported by the tested Full SFT or chat configurations.