Packages

All the Modular APIs and tools (including MAX and Mojo) are included in a single Python/Conda package named modular. This page explains what's in the package and shows how to install it.

If you just want to get started with Modular, instead see our quickstart guide.

New! You can now install a standalone mojo package.

Package managers

You can install modular as a Python or Conda package. The install instructions below include commands for popular package managers such as pixi, uv, pip, and conda, but you can also use something else.

Traditional tools like pip and conda might be necessary for existing production environments or Docker containers, but for your local development, we recommend using Pixi.

Pixi is both a package manager and virtual environment manager, which alone makes development a lot easier, but it's also language agnostic, extremely fast, and includes lock files to easily reproduce your project environment.

You'll notice that our GitHub code examples include a pixi.toml file. This file configures the environment to make sure we all use the same packages and get the same results—you just need to install pixi.

So if you're not set on using a particular package manager, we suggest you try pixi. If you haven't used it before, check out our Pixi basics guide or the official Pixi docs.

Install

To get the latest improvements and new features, we recommend installing our nightly build, which we release several times a week. If you want a better tested but older version, you can install a stable build. (Each release is described in the changelog.)

We recommend installing with pixi for the most reliable experience.

pixi

1. If you don't have it, install pixi:

   curl -fsSL https://pixi.sh/install.sh | sh

   Then restart your terminal for the changes to take effect.

2. Create a project:

   pixi init example-project \
     -c https://conda.modular.com/max-nightly/ -c conda-forge \
     && cd example-project

   Tip: You can skip the -c options if you add these channels as defaults.

3. Install the modular conda package:
   Nightly

   pixi add modular

   Stable

   pixi add "modular=25.5"

4. Start the virtual environment:

   pixi shell

uv

1. If you don't have it, install uv:

   curl -LsSf https://astral.sh/uv/install.sh | sh

   Then restart your terminal to make uv accessible.

2. Create a project:

   uv init example-project && cd example-project

3. Create and start a virtual environment:

   uv venv && source .venv/bin/activate

4. Install the modular Python package:
   Nightly

   uv pip install modular \
     --index-url https://dl.modular.com/public/nightly/python/simple/ \
     --prerelease allow

   Stable

   uv pip install modular \
     --extra-index-url https://modular.gateway.scarf.sh/simple/

pip

1. Create a project folder:

   mkdir example-project && cd example-project

2. Create and activate a virtual environment:

   python3 -m venv .venv/example-project \
     && source .venv/example-project/bin/activate

3. Install the modular Python package:
   Nightly

   pip install --pre modular \
     --index-url https://dl.modular.com/public/nightly/python/simple/

   Stable

   pip install modular \
     --extra-index-url https://modular.gateway.scarf.sh/simple/

conda

1. If you don't have it, install conda. A common choice is with brew:

   brew install miniconda

2. Initialize conda for shell interaction:

   conda init

   If you're on a Mac, instead use:

   conda init zsh

   Then restart your terminal for the changes to take effect.

3. Create a project:

   conda create -n example-project

4. Start the virtual environment:

   conda activate example-project

5. Install the modular conda package:
   Nightly

   conda install -c conda-forge -c https://conda.modular.com/max-nightly/ modular

   Stable

   conda install -c conda-forge -c https://conda.modular.com/max/ modular

The modular package installs MAX, Mojo, and other package dependencies.

GitHub stable branch

If you're using a stable build and want to clone the Modular repo, make sure you checkout the stable branch (because the main branch includes the latest nightly code). For example:

git clone -b stable https://github.com/modular/modular.git

Uninstall

pixi

You can uninstall modular from your virtual environment with this command:

pixi remove modular

To deactivate your virtual environment, run:

exit

uv

You can uninstall modular from your virtual environment with the following command:

uv pip uninstall modular

To deactivate your virtual environment, run:

deactivate

pip

You can uninstall modular from your virtual environment with the following command:

pip uninstall modular

To deactivate your virtual environment, run:

deactivate

conda

You can uninstall modular from your virtual environment with this command:

conda remove modular

To deactivate your virtual environment, run:

deactivate

What's included

Here's a summary of what's in the modular package.

conda/pixi

The modular Conda package installs the following:

• MAX tools and libraries:
  • max CLI
  • max Python library
  • max Mojo library
  • MAX Engine C API
• The mojo package:
  • mojo CLI (includes the Mojo compiler)
  • Mojo standard library
  • Mojo language server (LSP) for IDE/editor integration
  • Mojo debugger (includes LLDB)
  • Mojo code formatter
  • Mojo REPL

pixi known issues:

• You might encounter issues if you invoke pixi within a conda virtual environment. It's best if you don't mix the two tools.

pip/uv

The modular Python wheel installs the following:

• MAX tools and libraries:
  • max CLI
  • max Python library
  • max Mojo library
• The mojo package:
  • mojo CLI (includes the Mojo compiler)
  • Mojo standard library
  • Mojo language server (LSP) for IDE/editor integration
  • Mojo debugger (includes LLDB)
  • Mojo code formatter
  • Mojo REPL

Note

For information about the mojo package, see the Mojo install guide.

System requirements

To install modular, your system must meet these specifications.

Mac

• macOS Ventura (13) or later
• Apple silicon (M1/M2/M3/M4 processor)
• Python 3.9 - 3.13
• Xcode or Xcode Command Line Tools
• We currently don't support Mac GPUs

Linux

• Ubuntu 22.04 LTS
• x86-64 CPU (with SSE4.2 or newer) or AWS Graviton2/3 CPU
• Minimum 8 GiB RAM (or much more, depending on the model you run)
• Python 3.9 - 3.13
• g++ or clang++ C++ compiler
• To use GPUs, see the GPU compatibility

Windows

Windows is not officially supported at this time.

In the meantime, you can try MAX on Windows with WSL, using a compatible version of Ubuntu (see our requirements for Linux).

GPU compatibility

The Modular Platform supports both CPUs and GPUs, so you don't always need a GPU to serve a model or program with Mojo. But if you do want to accelerate your model with GPUs or program for GPUs with Mojo, we support the following GPUs.

Because we don't test every variant of a GPU architecture, and support for new architectures will improve incrementally, we've divided our list of compatible GPUs into 3 tiers:

Tier 1: Fully supported

We provide full support and testing for the following data center GPUs:

• NVIDIA B200 (Blackwell)
• NVIDIA H100 and H200 (Hopper)
• NVIDIA A100 and A10 (Ampere)
• AMD Instinct MI355X (CDNA4)
• AMD Instinct MI300X and MI325X (CDNA3)

Tier 2: Confirmed compatibility

We've confirmed full compatibility with the following GPUs but we currently don't maintain tests for them:

• NVIDIA L4 and L40 (Ada Lovelace)
• NVIDIA RTX 40XX series (Ada Lovelace)
• NVIDIA RTX 30XX series (Ampere)

Tier 3: Limited compatibility

We've either confirmed or received reports that the following GPUs work for GPU programming with Mojo and can execute basic graphs with MAX APIs. However, these GPUs currently can't run some GenAI models for various reasons:

• NVIDIA Tesla P100 (Pascal)
• NVIDIA RTX 20XX series (Turing)
• NVIDIA T4 (Turing)
• NVIDIA Jetson Orin and Orin Nano (Ampere)
• NVIDIA GeForce RTX 3090 (Ampere)
• NVIDIA GeForce GTX 1080 Ti (Pascal)
• AMD Radeon 700M series (RDNA3)
• AMD Radeon RX 7000 series (RDNA3)
• AMD Radeon RX 9000 series (RDNA4)

If you've had success with any GPUs not listed here, please let us know on Discord.

GPU software requirements

Make sure you have the corresponding GPU software:

• If you're on an NVIDIA GPU:

  • NVIDIA GPU driver version 550 or higher

    • Check your NVIDIA GPU driver version using nvidia-smi
    • To update, see the NVIDIA driver docs

• If you're on an AMD GPU:

  • AMD GPU driver version 6.3.3 or higher

    • For datacenter GPUs (MI300X/MI325X), see the Ubuntu native install guide
    • For Radeon GPUs on Ubuntu, see the Linux install guide for Radeon software
    • For Radeon GPUs on WSL, see the WSL install guide for Radeon software

Notes

• Many GPUs are available in variants with different amounts of memory, and each AI model has different memory requirements. So even if your GPU architecture is listed as compatible, you must confirm that the available memory is sufficient for the model you're using.

• Modular can serve lots of models on either CPU and GPU, but some models do require one or more GPUs. When you browse our model repository, you can filter by models that support either CPU or GPU.

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