Supported GitLab Duo Self-Hosted models and hardware requirements

GitLab Duo Self-Hosted supports integration with industry-leading models from Mistral, Meta, Anthropic, and OpenAI through your preferred serving platform.

You can choose from these supported models to match your specific performance needs and use cases.

In GitLab 18.3 and later, you can also bring your own compatible model, giving you the flexibility to experiment with additional language models beyond the officially supported options.

Supported models

Support for the following GitLab-supported large language models (LLMs) is generally available.

• Fully compatible: The model can likely handle the feature without any loss of quality.
• Largely compatible: The model supports the feature, but there might be compromises or limitations.
• Not compatible: The model is unsuitable for the feature, likely resulting in significant quality loss or performance issues. Models that are marked not compatible for a feature will not receive GitLab support for that specific feature.

Footnotes:

1. This model is scheduled for deprecation in GitLab 18.5. Mistral Small 24B Instruct 2506 is the recommended alternative.

Bring your own compatible model

You can bring your own compatible models to use with GitLab Duo features.

The general model family provides support for compatible models and platforms that adhere to the OpenAI API specification. Use this model family to try language models that are not explicitly supported by GitLab.

This feature is in beta and is therefore subject to change as we gather feedback and improve the integration:

• GitLab does not provide technical support for issues specific to your chosen model or platform.
• Not all GitLab Duo features are guaranteed to work optimally with every compatible model.
• Response quality, speed, and performance overall might vary significantly based on your model choice.

Experimental and beta models

The following models are configurable for the functionalities marked below, but are currently in beta or experimental status, under evaluation, and are excluded from the “Customer Integrated Models” definition in the AI Functionality Terms:

GitLab AI vendor models

GitLab AI vendor models integrate with GitLab-hosted AI gateway infrastructure to provide access to AI models curated and made available by GitLab. Instead of using your own self-hosted models, you can choose to use GitLab AI vendor models for specific GitLab Duo features.

To choose which features use GitLab AI vendor models, see Configure GitLab AI vendor models.

When enabled for a specific feature:

• All calls to those features configured with a GitLab AI vendor model use the GitLab-hosted AI gateway, not the self-hosted AI gateway.
• No detailed logs are generated in the GitLab-hosted AI gateway, even when AI logs are enabled. This prevents unintended leaks of sensitive information.

Hardware requirements

The following hardware specifications are the minimum requirements for running GitLab Duo Self-Hosted on-premise. Requirements vary significantly based on the model size and intended usage:

Base system requirements

• CPU:
  • Minimum: 8 cores (16 threads)
  • Recommended: 16+ cores for production environments
• RAM:
  • Minimum: 32 GB
  • Recommended: 64 GB for most models
• Storage:
  • SSD with sufficient space for model weights and data.

GPU requirements by model size

Use Hugging Face’s memory utility to verify memory requirements.

Response time by model size and GPU

Small machine

With a a2-highgpu-2g (2x Nvidia A100 40 GB - 150 GB vRAM) or equivalent:

Medium machine

With a a2-ultragpu-4g (4x Nvidia A100 40 GB - 340 GB vRAM) machine on GCP or equivalent:

Large machine

With a a2-ultragpu-8g (8 x NVIDIA A100 80 GB - 1360 GB vRAM) machine on GCP or equivalent:

AI Gateway Hardware Requirements

For recommendations on AI gateway hardware, see the AI gateway scaling recommendations.