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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 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 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 24.4 - Introducing quantization APIs and MAX on macOS
No items found. · 2024-06-07 · via Modular Blog

Today, we're thrilled to announce the release of MAX 24.4, which introduces a powerful new quantization API for MAX Graphs and extends MAX’s reach to macOS. Together, these unlock a new industry standard paradigm where developers can leverage a single toolchain to build Generative AI pipelines locally and seamlessly deploy them to the cloud, all with industry-leading performance. Leveraging the Quantization API reduces the latency and memory cost of Generative AI pipelines by up to 8x on desktop architectures like macOS, and up to 7x on cloud CPU architectures like Intel and Graviton, without requiring developers to rewrite models or update any application code.

Release highlights:

  • New Quantization API for MAX Graphs: Reduce LLM inference latency by 7x compared to llama.cpp context encoding through decreased memory usage and improved performance with the new Quantization API. MAX Graph supports BF16, INT4, and INT6 quantization, including K-Quantization.
  • Llama on MAX: MAX 24.4 includes new implementations of  Llama 3 and Llama 2, demonstrating the full power of MAX Graphs and the Quantization API!
  • MAX on macOS: Everything you love about MAX is now available on Apple silicon, further enhancing MAX’s programmability and portability. Developers can build state-of-the-art AI pipelines locally and seamlessly deploy them to cloud systems like Intel x86 and ARM Graviton.
  • Mojo 🔥 Improvements: Mojo 24.4 features several performance improvements to the core language and standard library. For the core language, def functions are more flexible, and advanced users will appreciate more advanced loop unrolling features, the ability to return safe references, and many others.
  • Community-Driven Innovation: Mojo 24.4 features 214 community pull requests from 18 contributors, with 30 contributed features accounting for 11% of all improvements in the latest Mojo release. These include performance and quality-of-life improvements to the standard library collections, new data and filetype operations, and updates to SIMD bitwise operations. You can find a complete list of contributors and enhancements in the Mojo 24.4 release notes.

You can get started with MAX 24.4 now through the Modular developer console or by installing it directly from your terminal:

Bash

curl -s https://get.modular.com | sh -

Head over to the MAX documentation for complete instructions on installing or updating MAX.

Quantization API

The new MAX Quantization API is a huge step in bringing state-of-the-art performance to models built with MAX Graphs.

Why does quantization matter?

Token generation in LLMs is memory-bound, and reducing the size of the model weight from FP32 to INT4 proportionally improves performance without significantly impacting model quality. Reduced model size also decreases the hardware requirements for running LLMs, making models more widely available and cost-effective to run.

MAX’s quantization API makes transitioning from full precision to INT4 quantization easier, a massive win for the MAX and Mojo developer communities.

New Quantized Llama Models

To highlight the power of MAX’s quantization API, we’re releasing two new quantized LLMs as part of the MAX 24.4 release: Llama 3 and Llama 2. These models are built entirely in Mojo 🔥 using the MAX Graph API. These models are the first of a series to meet a need for State-Of-The-Art LLMs that are performant and portable across all CPU types.

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You can download Llama 3 now and try it out!

Bash

# get the latest MAX examples from GitHub git clone https://github.com/modularml/max.git # navigate to the llama3 pipeline cd max/examples/graph-api/pipelines/llama3 # run INT4 quantized llama3! mojo ../../run_pipeline.🔥 llama3 \     --prompt "I believe the meaning of life is"

Read more in the MAX Getting Started guide.

MAX on MacOS

MAX is now available for macOS, delivering the full suite of acceleration and inference APIs to Apple silicon. This includes the new, fully quantized Llama3 model, which has a more than 8x performance boost in context encoding using INT4 compared to F32.

Developers can seamlessly transition from building SOTA models on their development machines to putting them into production on Intel x86 and ARM Graviton cloud-serving infrastructure. We’re excited to expand the portfolio of hardware platforms supported by MAX, delivering on the promise of programmability and portability. You can get started with MAX on macOS today!

New Developer Resources

To support our growing community of developers and users, we’ve completely reworked our documentation to focus on the user journey with MAX. There’s now a single API reference to cover the entire MAX platform and a new Getting Started guide that makes it faster and easier to get MAX up and running. This new experience makes it easier to understand and use all of MAX’s capabilities, and we’re excited to see what the Mojo and MAX communities will build!

In addition to the refreshed docs, we’re excited to announce Modular AI resources: a centralized hub for the latest and most relevant research papers on LLMs, Generative AI, and optimized ML systems.

🚀 Get Started with MAX 24.4!

Download MAX 24.4 now to get started with the new MAX Graph Quantization API, and start accelerating your models now. Read the docs to learn more, and check out our examples on how to run llama3 with the MAX Engine.

We’re excited to see what you build with MAX 24.4 ⚡️ and Mojo 🔥!

Until next time! 🔥