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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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(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 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? 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MAX 24.5 - With SOTA CPU Performance for Llama 3.1
No items found. · 2024-09-13 · via Modular Blog

September 13, 2024

Modular Team

We’re excited to announce the release of MAX 24.5, which ships with significant improvements to Llama 3.1 CPU performance, new Python graph API bindings, our biggest update to Mojo ever, industry-standard packaging, and a clarified license. Read on to learn more!

MAX 24.5 marks our final CPU-only release and ships with an improved Llama 3.1 pipeline, featuring up to 45% improved token generation over the 24.4 release*. This improvement is made possible by the addition of the new MAX Driver interface, which gives developers more control over the MAX engine and the accelerators it controls.

In addition to this improved performance, the MAX Llama pipeline is rebuilt from the ground up using a new technology preview of the Python graph API bindings, bringing the power of MAX directly to Python developers.

Get started with MAX 24.5 and the Llama 3.1 pipeline today using Magic, our new package manager. Magic delivers MAX and Mojo as a single package and gives you access to thousands of community-built packages for Python and other languages. You can install Magic with a single command from the Modular getting started page.

Once you have Magic installed, run the following commands to experience state-of-the-art performance on Llama 3.1 on CPUs:

That’s it! Magic automatically handles setting up MAX, installing the required dependencies, setting up an isolated virtual environment, and launching the Llama pipeline.

In addition, this release of MAX ships with several improvements, including:

  • A unified MAX and Mojo package based on industry-standard Conda packaging, with a 30% reduction in download size.
  • MAX now works with your choice of PyTorch, delivering an even more streamlined and interoperable experience.
  • An update to MAX and Mojo’s Community License, that outlines the many use cases you're empowered to build and monetize with MAX and Mojo. You can learn more in our licensing FAQ.
  • A new documentation site, including a growing collection of tutorials, examples, and getting started guides.
  • Support for Python 3.12, making MAX more widely available to developers.
  • Our biggest update to Mojo ever, with streamlined language features, performance improvements across the core language, and new standard library APIs that add features for strings, collections, and system interactions. Check out the full release notes to learn about these improvements, and so much more!

MAX 24.5 with Magic is available today! Download it now, experience state-of-the-art LLM performance with Llama 3.1 on CPUs, and get ready for what's coming in the next release.

* Up to 45% performance improvement of Llama 3.1 token generation over MAX 24.4 on a macOS M2 processor using Q4_K quantization. Up to 35% improvement on Graviton systems (c7g.16xlarge), and up to 20% improvement on Intel (c6i.16xlarge).

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