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Modular Blog

Qualcomm to Acquire Modular Modular 26.4: SOTA MoE Serving, Model Bringup via Agent Skills, Mojo 1.0 Beta 2 and More ModCon 2026: Modular’s Developer Conference Day Zero: MiniMax M3 Open Weights on Modular Cloud Modverse #55: Mojo 1.0 Beta, Community Mojo Libraries, and Real-Time Patient Conversations Powered by MAX What about OpenCL and CUDA C++ alternatives? (Democratizing AI Compute, Part 5) Why LLM Inference Needs a New Kind of Router - Part 3 Three trends from MLSys 2026 Why LLM Inference Needs a New Kind of Router - Part 2 How I built a pure Mojo app (and 10 libraries) with AI agents Hippocratic AI partners with Modular to power flexible, high-quality inference for real-time patient conversations Translating to Mojo via AI Agents Inkwell: Why Your Inference Platform Matters As Much As Your Model Why LLM Inference Needs a New Kind of Router - Part 1 Modverse #54: AMD AI DevDay, New Modular Offices, and a Community That Keeps Shipping How Frontier Coding Agents Built a Video Diffusion Pipeline on MAX TileTensor Part 1 - Safer, More Efficient GPU Kernels Modular Opens Edinburgh & San Francisco Offices Structured Mojo Kernels Part 4 - Portability and the Road Ahead Day Zero Launch: Fastest Performance for Gemma 4 on NVIDIA and AMD Modverse #54: From GTC to Edinburgh, a Community Building Momentum Software Pipelining for GPU Kernels: Part 1 - The Pipeline Problem Structured Mojo Kernels Part 3 - Composition in Practice Modular 26.2: State-of-the-Art Image Generation and Upgraded AI Coding with Mojo Modular at NVIDIA GTC 2026: MAX on Blackwell, Mojo Kernel Porting, and DeepSeek V3 on B200 Structured Mojo Kernels Part 2 - The Three Pillars Modverse #53: Community Builds, Research Milestones, and a Growing Ecosystem Structured Mojo Kernels Part 1 - Peak Performance, Half the Code The Claude C Compiler: What It Reveals About the Future of Software BentoML Joins Modular The Five Eras of KVCache Modular 26.1: A Big Step Towards More Programmable and Portable AI Infrastructure How to Beat Unsloth's CUDA Kernel Using Mojo—With Zero GPU Experience 🔥 Modular 2025 Year in Review The path to Mojo 1.0 Modverse #52: Advancing AI Together — Community Projects & Platform Milestones Modular 25.7: Faster Inference, Safer GPU Programming, and a More Unified Developer Experience "TTS 1 Max" (powered by Modular Platform) Ranked #1 Speech Model on Artificial Analysis PyTorch and LLVM in 2025 — Keeping up With AI Innovation Achieving State-of-the-Art Performance on AMD MI355 — in Just 14 Days Modular Raises $250M to scale AI's Unified Compute Layer Modular 25.6: Unifying the latest GPUs from NVIDIA, AMD, and Apple Matrix Multiplication on Blackwell: Part 4 - Breaking SOTA Modverse #51: Modular x Inworld x Oracle, Modular Meetup Recap and Community Projects Matrix Multiplication on Blackwell: Part 3 - The Optimizations Behind 85% of SOTA Performance Matrix Multiplication on Blackwell: Part 2 - Using Hardware Features to Optimize Matmul Matrix Multiplication on Blackwell: Part 1 - Introduction Modverse #50: Modular Platform 25.5, Community Meetups, and Mojo's Debut in the Stack Overflow Developer Survey Modular Platform 25.5: Introducing Large Scale Batch Inference SF Compute and Modular Partner to Revolutionize AI Inference Economics AI Agents for AWS Marketplace Modverse #49: Modular Platform 25.4, Modular 🤝 AMD, and Modular Hack Weekend Inside Modular Hack Weekend: Top Projects and Community Highlights How is Modular Democratizing AI Compute? (Democratizing AI Compute, Part 11) Modular 25.4: One Container, AMD and NVIDIA GPUs, No Lock-In Introducing Mammoth: Enterprise-Scale GenAI Deployments Made Simple Modular + AMD: Unleashing AI performance on AMD GPUs Modverse #48: Modular Platform 25.3, MAX AI Kernels, and the Modular GPU Kernel Hackathon Exploring Metaprogramming in Mojo Modular GPU Kernel Hackathon Highlights: Innovation, Community, & Mojo🔥 Modular’s bet to break out of the Matrix (Democratizing AI Compute, Part 10) Modular Platform 25.3: 450K+ Lines of Open Source Code and pip Packaging A New, Simpler License for MAX and Mojo Why do HW companies struggle to build AI software? (Democratizing AI Compute, Part 9) Modverse #47: MAX 25.2 and an evening of GPU programming at Modular HQ What about the MLIR compiler infrastructure? (Democratizing AI Compute, Part 8) What about Triton and Python eDSLs? (Democratizing AI Compute, Part 7) MAX 25.2: Unleash the power of your H200's–without CUDA! What about TVM, XLA, and AI compilers? (Democratizing AI Compute, Part 6) Modverse #46: MAX 25.1, MAX Builds, and Democratizing AI Compute CUDA is the incumbent, but is it any good? (Democratizing AI Compute, Part 4) MAX 25.1 - Introducing MAX Builds How did CUDA succeed? (Democratizing AI Compute, Part 3) Paged Attention & Prefix Caching Now Available in MAX Serve What exactly is “CUDA”? (Democratizing AI Compute, Part 2) Modular DeepSeek's Impact on AI (Democratizing AI Compute, Part 1) Modular Hands-on with Mojo 24.6 Evaluating Llama Guard with MAX 24.6 and Hugging Face Modular Introducing MAX 24.6: A GPU Native Generative AI Platform MAX GPU: State of the Art Throughput on a New GenAI platform Understanding SIMD: Infinite Complexity of Trivial Problems Community Spotlight: Writing Mojo with Cursor Hands-on with Mojo 24.5 MAX 24.5 - With SOTA CPU Performance for Llama 3.1 Announcing stack-pr: an open source tool for managing stacked PRs on GitHub Debugging in Mojo🔥 Write hardware-agnostic custom ops for PyTorch | Modular Take control of your AI Develop locally, deploy globally A brief guide to the Mojo n-body example What's new in MAX 24.4? MAX on macOS, fast local Llama3, native quantization and GGUF support What’s new in Mojo 24.4? Improved collections, new traits, os module features and core language enhancements MAX 24.4 - Introducing quantization APIs and MAX on macOS Deep dive into ownership in Mojo What ownership is really about: a mental model approach Fast⚡k-means clustering in Mojo🔥: a guide to porting Python to Mojo🔥 for accelerated k-means clustering
Modular 26.3: Mojo 1.0 Beta, MAX Video Gen, and more
No items found. · 2026-05-07 · via Modular Blog

May 7, 2026

Modular Team

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.

Mojo 1.0: now in beta! 🔥🔥🔥

Mojo is the foundation for everything we do at Modular, from pushing the state-of-the-art in kernel performance, to running on new and novel accelerator hardware. Back in December, we provided a roadmap to 1.0 for the Mojo language, and we’re now excited to announce that a beta for Mojo 1.0 is available today!

Mojo 1.0 will be finalized later this year, along with opening the compiler and providing language stability. This marks the start of a new era for the language. You can now build your projects against known versions of Mojo and they won’t break on you tomorrow. The beta provides what we believe is a “feature complete” Mojo 1.0 language, but there’s a lot to be polished before final release.

The 1.0 beta ships several features we've been working toward for a long time. Those include:

  • Safe closures with a new capturing syntax.
  • Conditional conformance to traits.
  • Major improvements to variadics.

We’re also introducing the successor to LayoutTensor, called TileTensor, that makes it even easier to write high-performance kernels. TileTensor makes memory layout a compile-time property of the tensor itself, so the swizzles, strides, and indexing that GPU kernels require are checked by the type system rather than maintained by hand. We’ve started a dedicated blog post series on this neat new type, and it underpins the new paradigm of structured kernels we write about in our ongoing series.

But that’s not all! We felt that with how far Mojo has come, it was time to give it a proper new home.

Introducing mojolang.org

That’s right, Mojo now has its own website at mojolang.org!

Along with the Mojo 1.0 beta release, launching this website represents a significant milestone; Mojo is nearly ready for widespread adoption. Mojolang.org is an important step towards opening up Mojo to the world with a full 1.0 release, which we aim to complete by the fall.

Whether you’re new to Mojo or an experienced contributor, it’s now more clear than ever where to get everything you need.

With all the Mojo documentation on its own site, docs.modular.com is now focused on what you need to build and serve models with MAX. You only need Mojo with MAX if you’re extending or building custom kernels, which is why the MAX AI kernels library is still at docs.modular.com. For a full look at all the new Mojo updates that accompany the beta, see the mojolang.org changelog.

Video Generation in MAX

It’s not just Mojo that has major updates. We’re adding a new modality to the unified Modular platform: video generation.

We started with text, expanded to audio, added image generation / editing, and with the addition of video generation, if your application needs to move from a static image to a living scene, you no longer need to step outside the Modular Platform to do it.

Today's release brings support for Wan 2.2, one of the leading open video generation models, with many more coming very soon, alongside multiple improvements. Video generation with MAX is available today and will be coming soon to Modular Cloud. If you're building video into your workflow and want to discuss what this means for your infrastructure, contact us.

MAX Framework

Unified, distributed-aware tensor

Real-world models now routinely span multiple GPUs. In 26.3 we've expanded multi-GPU support in max.experimental: a distributed-aware Tensor type, multi-device compilation, and the collective ops you need for tensor-parallel code.

PyTorch's DTensor established that placement metadata on a tensor is the right abstraction for reasoning about distribution. JAX's jax.Array showed how readable a single tensor type plus named mesh axes can be. MAX borrows from both and adds one thing neither has: the same .to(...) call accepts both a NamedMapping (JAX-style "this tensor axis maps to that mesh axis") and a PlacementMapping (DTensor-style Replicated / Sharded / Partial). You pick whichever fits the problem; both lower to the same representation.

The practical result: Tensor is the same type whether it lives on one device or is sharded across a mesh. Sharding is metadata, not a separate code path.

Additional highlights

  • MAX’s fast eager interpreter reaches 100% operator coverage for eager mode: until recently, graphs in MAX had to go through the full compiler before they could run, we’re fixing that with the MO graph interpreter in max.experimental a 10-20x faster path for eager execution. In 26.3, we've completed the remaining operator coverage: gather/scatter (embedding lookups, sparse updates), convolution and pooling (ConvOp, MaxPoolOp, AvgPoolOp), arg/search ops (ArgMaxOp, ArgMinOp, TopKOp), data rearrangement (SplitOp, TileOp), and all other previously missing handlers. We’ll further improve this in 26.4.
  • The NVFP4 grouped matmul kernels were tuned across all tested shapes; layer_norm, topk, argsort, concat, and pad_constant GPU kernels were tuned; Mojo implementation of the Programming Massively Parallel Processors(PMPP) textbook examples now ship.
  • max benchmark gained a sweep mode (concurrency x request-rate, JSON output), KV connector flags moved into -kv-connector-config, -model-override enables mixed-quant diffusion pipelines, and Float8Config is now QuantConfig (FP8 + NVFP4 + MXFP4).

For a full list of changes see the MAX and Mojo changelogs

Get started with 26.3

Modular 26.3 is available now, launching Mojo 1.0 Beta, bringing high-performance video generation to MAX, improving MAX’s developer experience, and simplifying Mojo syntax for closures and memory tiling. Install or upgrade to get started in minutes:

Install or upgrade to get started in minutes:

shell

uv pip install --upgrade modular

For a deeper look at everything included in this release, check out:

If you’re building with Modular, join us on:

We’re excited to hear about what you build with 26.3, and with the Mojo beta.

Share your feedback on the Mojo 1.0 beta:

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