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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 Modular 26.3: Mojo 1.0 Beta, MAX Video Gen, and more 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) 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 Platform 25.3: 450K+ Lines of Open Source Code and pip Packaging
No items found. · 2025-05-06 · via Modular Blog

Today we’re excited to announce Modular Platform 25.3, a significant advancement for MAX and Mojo as an integrated suite of AI libraries and tools designed to unify the AI deployment workflow. With the extensive expansion of our open source libraries and a new unified pip package, Modular Platform 25.3 makes high-performance AI more accessible and community-driven.

🔓 Open sourcing kernels, the Mojo standard library, and serving APIs

Most notably in Modular 25.3, we’re releasing the MAX AI kernels and the full Mojo standard library under the Apache 2.0 License (with LLVM exceptions). These libraries include thousands of lines of high-performance, hardware-optimized Mojo code, including production-grade kernel implementations for various CPUs and GPUs, including NVIDIA’s T4, A10G, L40, RTX 40 series, Jetson Orin Nano, A100, H100, and more. Our quantization schemes include Q4_K, Q4_0, Q6_K, GPTQ, and FP8, offering cost-effective performance for demanding workloads.

Additionally, we've open sourced the MAX serving library, our inference server that supports OpenAI-compatible endpoints and enables efficient LLM serving at scale. Together, these releases form an open, extensible AI inference stack free from proprietary GPU dependencies.

By making this new code public we’ve now collectively open sourced more than 450K lines of code from almost 6500 contributions, providing developers with production-grade reference implementations and tools to extend Modular Platform with new algorithms, operations, and hardware targets. With so much novel high-performance code, you can fine-tune your LLMs to “vibe code” with Mojo and harness the full power of modern AI hardware. We’ve found Claude Code to be particularly powerful for writing Mojo when given all this context!

We believe this is the single largest open sourcing of CPU and GPU kernels ever! To celebrate this release, Modular is hosting a community hackathon with AGI House and Crusoe GPU Cloud on May 10 at AGI House in Hillsborough, focusing on programming next-generation GPU kernels with Mojo.

🐍 Simplified installation with pip and Colab support

We’re also releasing a significant new improvement to Modular Platform: pip-based packaging. With a simple pip install modular , you gain immediate access to Mojo, our high-performance CPU and GPU programming language, and MAX, our fast AI serving framework. This pip packaging deepens our integration with the Python ecosystem, making it even easier to get started using Mojo and MAX for your critical AI workloads.

Making Modular available in PyPI has been no easy feat, and today we are beyond excited to have native support for pip. As one of only 100 companies with an Enterprise PyPI account, we’re supporting the Python developer ecosystem with a direct financial contribution and are committed to maintaining the highest standards for package quality, security, and documentation.

The release of pip install modular also unlocks an exciting new capability: running MAX models and graphs in Google Colab. The modular stable package supports running full LLMs in Google Colab Pro using A100 or L4 GPU instances. We also provide introductory support in our latest nightly build for GPU programming with MAX graphs on the free tier of Colab using T4 GPUs. Learn more about Colab support in the Modular forum.

The Modular pip packages are available today! Download it now, and be sure to share all the incredible code you develop in the Modular community forum.

📓 An updated usage license for a new era

To make our technology more accessible, we've simplified the community license for Mojo and MAX based on user feedback. Our straightforward tiered structure gives everyone freedom to use both Mojo and MAX with minimal restrictions. Watch our Community Event on the license update here.

For non‑production‑commercial use, everything is free. Use Mojo and MAX on any device, for any research, hobby or learning project. For production and commercial use, both Mojo and MAX remain free on CPUs and NVIDIA GPUs—we simply ask that you share your success story with us. For commercial deployment on non-NVIDIA accelerators, we provide free access for up to eight devices, with enterprise options available beyond that threshold. Extended use cases on other platforms require agreements with platform vendors on the best way to distribute MAX.

This update reflects our commitment to building in the open, lowering barriers to entry, and putting the community first as we enter a new era of "Build with Modular." Full details of our simplified license are available at modular.com/pricing and modular.com/legal/community.

🚀 Get started today & join us in person!

Ready to explore what Modular can do for your AI projects?

We can't wait to see what you'll build with Modular platform!

We invite you to join us on May 10 at AGI House in Hillsborough for the “Modular GPU Kernel Hackathon: Hands-on with Mojo,” where you'll get direct experience using Mojo for kernel development. Discover how to write cleaner, faster, and more portable code for the latest GPUs that can transform your AI systems programming.

For our open source releases: developers can contribute to the Mojo standard library today, and we’ll enable community contributions to the rest of the MAX libraries soon. We’re building the review and testing infrastructure to support upstream contributions to the kernel library, and are eager to welcome your contributions.

We’re excited to be at the cutting edge of kernel innovation and to lead the open movement that’s redefining what's possible in high-performance computing. This release reflects our commitment towards building a more open AI ecosystem. We invite you to come and build with us!