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Surprise: Mojo 1.0 is officially in beta! Modular’s 26.3 release includes new features and modalities, but the headline is that we’ve officially hit beta for Mojo 1.0, with a clear plan to finalize Mojo 1.0 in the coming months. We share details below, alongside other key announcements in our 26.3 release including video generation in MAX with Wan 2.2 and MAX framework updates.

There was a lot to celebrate in April: the community shipped GPU renderers, FFmpeg bindings, raylib wrappers, BLAS routines, and a 2D graphics API, just to name a few. The team connected with tons of developers at AMD AI DevDay and our joint meetup with AMD, two new Modular offices opened on two different continents, and Gemma 4 launched with same-day support on NVIDIA and AMD. Here’s the April roundup.

In a clear demonstration of how rapidly AI coding agents are becoming capable of challenging systems engineering work, two of the five agents produced a working MAX pipeline. The models we tested were:

Suppose you want to load a 2D tile of a matrix, where the tile is stored in shared memory in a specific interleaved layout to avoid bank conflicts. This example uses a toy XOR swizzle to illustrate the class of bugs; real kernels use hardware- and layout-specific swizzles and vectorized accesses. Without a layout abstraction, here is how you would launch a kernel with a block size of (32,8):

Modular is growing! We've opened offices in two cities with deep ties to the work we do.

GPU portability has a mixed track record. “Write once, run everywhere” usually means “write once, run slowly everywhere.” CUTLASS does not attempt portability beyond NVIDIA hardware and is usually limited within a generation of the hardware. Triton provides portability but performance degrades on non-NVIDIA targets. The conventional wisdom is that you have to choose between being portable or being fast.

Our benchmarks show 15% higher throughput when compared to vLLM on NVIDIA B200.

This edition covers one of the busiest stretches in Modular's recent history: four days at GTC, a new office on another continent, fresh community builds, and a release that expands what MAX and Mojo🔥 can do. Here's everything that's been happening across the ecosystem.

Flash Attention is a simple algorithm: tiled back-to-back matmuls with an online softmax algorithm in between. The algorithm fits in a few dozen lines of pseudocode. Yet Flash Attention 4's production kernel is 2,875 lines, and the hardest part to get right isn't the math. It's the async execution and pipelining synchronization, all hand-derived from a schedule that no standard debugging tool can verify.

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.