Models and hardware requirements

You can integrate with industry-leading models from Mistral, Meta, Anthropic, and OpenAI through your preferred serving platform.

You can use:

• Supported models to match your specific performance needs and use cases.
• In GitLab 18.3 and later, your own compatible model to experiment with models beyond the officially supported options.
• GitLab-managed models to connect to AI models without the need to host your own infrastructure. These models are managed entirely by GitLab.

Supported models

GitLab-supported models offer different levels of functionality for GitLab Duo features, depending on the specific model and feature combination.

• Full functionality: The model can likely handle the feature without any loss of quality.
• Partial functionality: The model supports the feature, but there might be compromises or limitations.
• Limited functionality: The model is unsuitable for the feature, likely resulting in significant quality loss or performance issues. Models that have limited functionality for a feature will not receive GitLab support for that specific feature.

Compatible models

You can use your own compatible models and platform with GitLab Duo features. For compatible models not included in supported model families, use the general model family. This includes models that you host yourself (for example, served through vLLM or LiteLLM), with the requirement that they are exposed through an OpenAI API–compatible /v1 endpoint.

Compatible models are excluded from the definition of Customer Integrated Models in the AI Functionality Terms. Compatible models and platforms must adhere to the OpenAI API specification. Models and platforms that have previously been marked as experimental or beta are now considered compatible models.

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.

GitLab-managed models

GitLab-managed 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-managed models for specific GitLab Duo features.

To choose which features use GitLab-managed models, see select a GitLab-managed model for a feature.

When enabled for a specific feature:

• All calls to those features configured with a GitLab-managed 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.