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This post shows the practical benefit of this modular design. We take two real kernel families, conv2d and block-scaled matmul, and trace exactly how they are built around the matmul foundation. In both cases, a new kernel family requires changing one component while leaving the rest untouched. The conv2d kernel adds roughly 130 lines of new code, whileBlock-scaled matmul adds roughly 200 with no performance degradation.

Today’s 26.2 release expands the Modular Platform’s modality support to include image generation and image editing workflows. This extends our existing support for text and audio generation. In the 26.2 version Black Forest Labs' FLUX.2 model variants are supported with over a 4x speedup over state-of-the-art.

Each spring, San Jose fills up with people who have strong opinions about GPUs, and we're happily among them. Find us this week at NVIDIA GTC, Booth #3004, where we’ll be running demos all week on Blackwell.

This post explains the components of Structured Mojo Kernels: TileIO, TilePipeline, and TileOp. Each component forms a node in a kernel execution pipeline, and the links between them create a logical separation of concerns that makes kernels easier to extend and update. That organization matters because GPU kernels don't stay static. By abstracting hardware optimized implementations into patterns, the same kernel structure can adapt across NVIDIA and AMD hardware generations with minimal rewrite.

This edition captures everything happening across the Modular ecosystem, from developers building with MAX and Mojo🔥 to the broader impact Modular is having across AI infrastructure. Here's a look at what's been happening lately.

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.