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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? 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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) 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
MAX 25.1 - Introducing MAX Builds
No items found. · 2025-02-18 · via Modular Blog

Today, we're excited to announce the release of MAX 25.1, marking a significant evolution in our approach to delivering cutting-edge AI development tools to our community. This release substantially improves the developer experience for Agentic and LLM workflows, introduces a new nightly release model that includes a new GPU programming interface, and launches MAX Builds - your one-stop destination for GenAI development.

MAX Builds

We're thrilled to announce MAX Builds, your comprehensive hub for GenAI models, application recipes, and community-driven packages. While we're still expanding our coverage, MAX Builds serves as your go-to resource for:

This new platform represents our commitment to making AI development more accessible and efficient for developers at every skill level. Our goal is to continue making AI simpler and more approachable, handling local to cloud deployment, along with a single stack that delivers incredible Gen AI performance.

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Come take a look, and tell us which models you want to see in MAX Builds!

MAX 25.1

MAX 25.1 introduces multiple groundbreaking improvements across several key areas:

Enhanced Agent and RAG Capabilities

The release brings substantial quality-of-life improvements that streamline the development process:

High-Performance LLM Workflows

We've implemented two significant performance enhancements:

  • Support for paged attention: by enabling larger cache sizes during token generation, users can see up to a 5% improvement in token generation performance. In addition, paged attention also improves memory efficiency, allowing for longer context lengths.
  • A preview of prefix caching: by caching the key-value (KV) computation of existing inference requests, new queries can reuse the context encoded in the KV cache, eliminating redundant computations and improving performance for workloads with repeated prefixes. In workloads that with repetitive inputs, prefix caching can commonly improve throughput by 30%.

You can read more about how to enable paged attention and prefix caching in the docs.

Offline Batch Inference

MAX 25.1 supports offline batched inference for LLM workflows, allowing you to load a model and run inference directly from Python. Offline batch inference improves performance by grouping requests together and removing the latency associated with HTTP requests. Expected performance depends on the exact workload, with smaller batch jobs demonstrating a 12% higher throughput relative to vLLM due to improved latency and model load time.

GPU Programming with Mojo via MAX Graphs

MAX is built on an exciting GPU programming interface that abstracts away the underlying hardware. MAX 25.1 introduces a new Custom Ops API that allows you to extend MAX Engine with new graph operations written in Mojo that execute on either CPU or GPU, providing full composability and extensibility for your models. Take a deep dive into the lastest GPU programming examples on GitHub!

Accelerating Innovation with MAX Nightlies

MAX 25.1 represents more than feature improvements–it marks a fundamental shift in our delivery approach. We're moving to a nightly-first model that emphasizes continuous innovation:

  • Immediate Feature Access: New capabilities will be available in MAX nightlies as soon as they're ready. New models, pipeline optimizations, and APIs are reach the nightlies first, with documentation and recipes available to get you started with the new feature.
  • Community-Driven Development: Early access enables real-time feedback from our community, helping shape features during development. Follow along with the development of the GPU programming and other new APIs in the MAX nightlies!

The MAX GitHub repository, release packages, and Docker images will now default to nightly builds, ensuring you can always access the latest improvements.

The year of MAX

This release marks an exciting start to 2025 for the MAX Platform. Whether you're building groundbreaking AI applications, contributing to our growing ecosystem, or exploring the possibilities of GenAI, MAX 25.1 provides the tools and infrastructure you need to succeed.

Ready to dive in? Visit our documentation to get started with MAX 25.1, explore MAX Builds, and join our community discussion on Discourse.

Come build with us!

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