Chris Lattner presents Mojo Max, a new AI software stack and programming language that combines Python-like usability with systems-level performance to enable efficient, portable GPU programming across hardware vendors like AMD and Nvidia. Mojo’s innovative metaprogramming and seamless Python integration allow developers to write high-performance, maintainable GPU code today, aiming to unify and simplify the fragmented AI software ecosystem.
In this talk, Chris Lattner introduces Mojo Max, a new native AI software stack developed by Modular, designed to unify usability, performance, and portability in AI programming, particularly targeting GPUs including AMD hardware. He emphasizes that unlike many evolving AI technologies, Mojo Max is available today for download and use, not just a vision. The goal is to rebuild the AI software stack from first principles to overcome the complexity and fragmentation seen in existing solutions like ROCm, OpenCL, and Triton, enabling developers to program GPUs efficiently regardless of hardware vendor.
Mojo is presented as a new programming language that combines the familiar, Pythonic syntax loved by many developers with the power and performance of a systems programming language. It addresses the shortcomings of Python in performance and GPU programming by offering a language that supports advanced GPU features, inline assembly, and metaprogramming, all while maintaining usability and developer productivity. Mojo’s design allows programmers to write high-performance GPU kernels with the ease of Python syntax but without the complexity and compile-time issues associated with C++ templates.
A key innovation in Mojo is its metaprogramming capabilities, which unify compile-time and runtime programming into a single coherent model. This allows developers to write generic, reusable, and highly optimized code that can be specialized at compile time without the typical pain points of C++ template programming. The language supports powerful abstractions, traits for better error messages, and compile-time parameters, enabling developers to build complex, high-performance GPU algorithms that are also easy to debug and maintain.
Mojo integrates seamlessly with the existing Python ecosystem, allowing developers to call Mojo code directly from Python without the need for complex bindings. This enables incremental adoption where developers can optimize performance-critical parts of their code in Mojo while continuing to use Python for the rest. The stack also supports heterogeneous hardware environments, including AMD and Nvidia GPUs, and is designed to scale from research to large-scale deployment with tools like Mammoth for managing fleets of GPUs efficiently.
Finally, Chris addresses questions about the choice of Python-like syntax over other languages, emphasizing the importance of meeting developers where they are and leveraging Python’s widespread use in AI. He also discusses the potential for Mojo to support other accelerators beyond GPUs in the future, though currently, the focus remains on CPUs and GPUs. The talk highlights the ambition of Modular to create a unified, high-performance, and portable AI programming environment that simplifies development and unlocks the full potential of modern hardware.
@artesia will mojo be open source?
@merefield, yes, Mojo is open-sourced and available on GitHub as part of the Modular Platform. You can find the repository here, which includes Mojo along with other tools for AI development. It’s licensed under the Apache License v2.0 with LLVM Exceptions.