• For self-hosted model deployments
• For cloud-hosted model deployments

Self-hosted models supported platforms

There are multiple platforms available to host your self-hosted Large Language Models (LLMs). Each platform has unique features and benefits that can cater to different needs. The following documentation summarises the currently supported options:

For self-hosted model deployments

1. vLLM. A high-performance inference server optimized for serving LLMs with memory efficiency. It supports model parallelism and integrates easily with existing workflows.
   • vLLM Installation Guide. We recommend installing version v0.6.4.post1 or later.
   • vLLM Supported Models

   For information on available options when using vLLM to run a model, see the vLLM documentation on engine arguments.

   For example, to set up and run the Mistral model, run the following command:

   HF_TOKEN=HUGGING_FACE_TOKEN python -m vllm.entrypoints.openai.api_server \
      --model mistralai/Mistral-7B-Instruct-v0.3 \
      --served-model-name Mistral-7B-Instruct-v0.3 \
      --tensor-parallel-size 8 \
      --tokenizer_mode mistral \
      --load_format mistral \
      --config_format mistral \
      --tokenizer mistralai/Mistral-7B-Instruct-v0.3
   

For cloud-hosted model deployments

1. AWS Bedrock. A fully managed service that allows developers to build and scale generative AI applications using pre-trained models from leading AI companies. It seamlessly integrates with other AWS services and offers a pay-as-you-go pricing model.
   • AWS Bedrock Model Deployment Guide
   • Supported foundation models in Amazon Bedrock
2. Azure OpenAI. Provides access to OpenAI’s powerful models, enabling developers to integrate advanced AI capabilities into their applications with robust security and scalable infrastructure.
   • Working with Azure OpenAI models
   • Azure OpenAI Service models