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GPU programming has a steep learning curve. The performance gains are massive, but the path to get there (CUDA, PTX, memory hierarchies, occupancy tuning) stops most developers before they start. Mojo claims to flatten that curve: Python-like syntax, systems-level performance, no interop gymnastics.

Our four-part series documenting the path to record-breaking matrix multiplication performance became essential reading for anyone serious about LLM optimization. The series walks through every optimization step—from baseline implementations to advanced techniques like warp specialization and async copies—showing you exactly how to extract maximum performance from cutting-edge hardware.

While we are excited about this milestone, this of course won’t be the end of Mojo development! Some commonly requested capabilities for more general systems programming won’t be completed for 1.0, such as a robust async programming model and support for private members. Read below for more information on that!

The Modular universe is buzzing! From next-level community projects to recognition across the AI and developer space, here’s the latest from our growing ecosystem.

Today, we’re excited to release Modular Platform 25.7, an update that deepens our vision of a unified, high-performance compute layer for AI. With a fully open MAX Python API, an experimental next-generation modeling API, expanded hardware support for NVIDIA Grace superchips, and a safer, more capable Mojo GPU programming experience, this release moves us closer to an ecosystem where developers spend less time fighting infrastructure and more time advancing what AI can do.

Modular Raises $250M in Third Round to Unify AI Compute

We’re excited to announce Modular Platform 25.6 – a major milestone in our mission to build AI’s unified compute layer. With 25.6, we’re delivering the clearest proof yet of our mission: a unified compute layer that spans from laptops to the world’s most powerful datacenter GPUs. The platform now delivers: