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GPU programming has always demanded precision, but the cost of that precision keeps rising. A production matmul kernel written in C++ spans 3,000–5,000 lines of tightly coupled code where a misplaced barrier silently corrupts results. That complexity gatekeeps hardware that should be available to far more developers, and it's a direct product of how GPUs have evolved: with each architecture generation, more of the orchestration burden has shifted onto the programmer.

Compilers occupy a special place in computer science. They're a canonical course in computer science education. Building one is a rite of passage. It forces you to confront how software actually works, by examining languages, abstractions, hardware, and the boundary between human intent and machine execution.

Today, BentoML is joining Modular.

vLLM, SGLang, TensorRT-LLM, and MAX Serve are all built on top of increasingly sophisticated KV cache management. This blog explores the evolution and role of the KV cache in these inference engines

Today we’re releasing Modular 26.1, a major step toward making high-performance AI computing easier to build, debug, and deploy across heterogeneous hardware. This release is focused squarely on developer velocity and programmability—helping advanced AI teams reduce time to market for their most important innovations.

Traditional 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 aims to flatten that curve: Python-like syntax, systems-level performance, no interop gymnastics, and the same performance gains.

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.