A unified platform for AI development and deployment, including MAX🧑🚀 and Mojo🔥.
The Modular Platform is an open and fully-integrated suite of AI libraries and tools that accelerates model serving and scales GenAI deployments. It abstracts away hardware complexity so you can run the most popular open models with industry-leading GPU and CPU performance without any code changes.
You don't need to clone this repo.
You can install Modular as a pip or conda package and then start an
OpenAI-compatible endpoint with a model of your choice.
If we trim the ceremonial steps, you can start a local LLM endpoint with just two commands:
pip install modularmax serve --model-path=modularai/Llama-3.1-8B-Instruct-GGUFThen start sending the Llama 3 model inference requests using our OpenAI-compatible REST API.
Or try running hundreds of other models from our model repository.
For a complete walkthrough, see the quickstart guide.
The MAX container is our Kubernetes-compatible Docker container for convenient
deployment, using the same inference server you get from the max serve
command shown above. We have separate containers for NVIDIA and AMD GPU
environments, and a unified container that works with both.
For example, you can start a container for an NVIDIA GPU with this command:
docker run --gpus=1 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
-p 8000:8000 \
docker.modular.com/modular/max-nvidia-full:latest \
--model-path modularai/Llama-3.1-8B-Instruct-GGUFFor more information, see our MAX container docs or the Modular Docker Hub repository.
We're constantly open-sourcing more of the Modular Platform and you can find all of it in here. As of May, 2025, this repo includes over 450,000 lines of code from over 6000 contributors, providing developers with production-grade reference implementations and tools to extend the Modular Platform with new algorithms, operations, and hardware targets. It is quite likely the world's largest repository of open source CPU and GPU kernels!
Highlights include:
- Mojo standard library: /mojo/stdlib
- MAX GPU and CPU kernels: /max/kernels (Mojo kernels)
- MAX inference server: /max/serve (OpenAI-compatible endpoint)
- MAX model pipelines: /max/pipelines (Python-based graphs)
- Code example: /examples
- Tutorials: /tutorials
This repo has two major branches:
-
The
mainbranch, which is in sync with the nightly build and subject to new bugs. Use this branch for contributions, or if you installed the nightly build. -
The
stablebranch, which is in sync with the last stable released version of Mojo. Use the examples in here if you installed the stable build.
Thanks for your interest in contributing to this repository!
We accept contributions to the Mojo standard library, MAX AI kernels, code examples, and Mojo docs, but currently not to any other parts of the repository.
Please see the Contribution Guide for instructions.
We also welcome your bug reports. If you have a bug, please file an issue here.
If you'd like to chat with the team and other community members, please send a message to our Discord channel and our forum board.
This repository and its contributions are licensed under the Apache License v2.0 with LLVM Exceptions (see the LLVM License). Modular, MAX and Mojo usage and distribution are licensed under the Modular Community License.
You are entirely responsible for checking and validating the licenses of third parties (i.e. Huggingface) for related software and libraries that are downloaded.