Supported self-hosted models and hardware requirements

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History

The following table shows the supported models along with their specific features and hardware requirements to help you select the model that best fits your infrastructure needs for optimal performance.

Approved LLMs

Install one of the following GitLab-approved LLM models:

Model family Model Code completion Code generation GitLab Duo Chat
Mistral Codestral Codestral 22B v0.1 Yes Yes No
Mistral Mistral 7B-it v0.3 Yes Yes Yes
Mistral Mixtral 8x7B-it v0.1 Yes Yes Yes
Mistral Mixtral 8x22B-it v0.1 Yes Yes Yes
Claude 3 Claude 3.5 Sonnet Yes Yes Yes
GPT GPT-4 Turbo Yes Yes Yes
GPT GPT-4o Yes Yes Yes
GPT GPT-4o-mini Yes Yes Yes

The following models are under evaluation, and support is limited:

Model family Model Code completion Code generation GitLab Duo Chat
CodeGemma CodeGemma 2b Yes No No
CodeGemma CodeGemma 7b-it No Yes No
CodeGemma CodeGemma 7b-code Yes No No
Code Llama Code-Llama 13b-code Yes No No
Code Llama Code-Llama 13b No Yes No
DeepSeek Coder DeepSeek Coder 33b Instruct Yes Yes No
DeepSeek Coder DeepSeek Coder 33b Base Yes No No
Mistral Mistral 7B-it v0.2 Yes Yes Yes

Hardware requirements

The following hardware specifications are the minimum requirements for running self-hosted models 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

Model size Recommended GPU configuration Minimum VRAM required
7B models
(for example, Mistral 7B)
1x NVIDIA A100 (40GB) 24 GB
22B models
(for example, Codestral 22B)
2x NVIDIA A100 (80GB) 90 GB
Mixtral 8x7B 2x NVIDIA A100 (80GB) 100 GB
Mixtral 8x22B 8x NVIDIA A100 (80GB) 300 GB

AI Gateway Hardware Requirements

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