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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 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 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? 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Day Zero Launch: Fastest Performance for Gemma 4 on NVIDIA and AMD
No items found. · 2026-04-02 · via Modular Blog

April 2, 2026

Modular Team

Today, Google DeepMind released the Gemma 4 family of models, the latest state-of-the-art open models from the same team behind Google Gemini models. We’re thrilled to be day zero launch partners and invite developers and enterprises to try out Gemma 4’s multimodal capabilities on Modular Cloud with state of the art performance on both NVIDIA and AMD.

Our benchmarks show 15% higher throughput when compared to vLLM on NVIDIA B200.

Meet the Gemma 4 family

Modular is hosting all variants of Google’s new Gemma 4 family of models. All are natively multimodal, supporting text, images, and video with dynamic resolution and aspect ratio.

Gemma 4 31B is a 31-billion-parameter dense model featuring a redesigned architecture that improves both efficiency and long-context quality. With a 256K context window, it's built for demanding tasks that require deep reasoning across large inputs.

Gemma 4 26B A4B is a Mixture-of-Experts (MoE)model with 26B total parameters but only 4B activated per forward pass, meaning you get the quality of a much larger model at a fraction of the compute cost. It also supports a 256K context window and is designed to fit the memory footprint of high-end servers.

Whether you're running OCR, video and image understanding, or 256K-context workflows, the same MAX-powered engine that handles your initial tests runs your production Modular Cloud endpoint, so there are no surprises when you scale.

High performance Gemma 4 on NVIDIA and AMD

Modular Cloud runs on MAX, our inference framework that unifies GPU kernels, graph compilation, and high-performance serving in a single hardware-agnostic platform. Modular allows teams to move fast: we optimized Gemma 4 to state-of-the-art across NVIDIA and AMD and brought up a production-ready endpoint on Modular Cloud within a few days.

Performance is a given in MAX, and on NVIDIA B200, Gemma 4 on MAX is 15% faster than vLLM while experiencing no accuracy degradation. Stay tuned for more details coming soon.

Accuracy MAX (B200)vLLM* (B200)MAX (MI355)
MMLU Pro84.72%(not measured)84.94%
GSM8K Llama COT95.94%94.69%94.37%
ChartQA84.69%84.38%84.38%

*Benchmarked using the officially provided vllm-0.18.2rc1.dev73

Try Gemma 4 on Modular Cloud Today

Gemma 4 is one of the most capable open models available - but capability only matters if you can ship it. Modular Cloud gives you a straight line from first API call to production endpoint, optimized across NVIDIA and AMD hardware, with no infrastructure archaeology required.

Try it immediately - use this chat window where we've opened up a 10-prompt free experience.

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With Modular Cloud, you can:

  • Transition from playground to production without switching stacks - The same MAX-powered engine that runs your tests runs your production endpoint.
  • Choose the right GPU for your workload - Run on NVIDIA or AMD, whichever fits your cost and throughput requirements. The stack picks the right configuration and batch sizing so you're not guessing.
  • Ship multimodal and long-context features faster - OCR, video + image understanding, and 256K-context workflows are supported out of the box.

Start building on Modular Cloud →

Or, try it for free yourself with MAX.

Interested in running MAX on your own infrastructure or in your own cloud? Book a demo and we'll walk through a deployment built around your models, hardware, and performance goals.

Discover what Modular can do for you

Request a demo

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