Modular

Modular provides a unified AI infrastructure platform designed to deliver state-of-the-art performance for generative AI workloads across multiple GPU vendors. The platform combines MAX (a GenAI serving framework), Mojo (a high-performance programming language), and Mammoth (a Kubernetes-native control plane for large-scale distributed AI serving) to enable developers to build, optimize, and deploy AI systems with unprecedented hardware portability.

• MAX Framework offers a GenAI serving framework that supports 500+ open models with customizable, open-source implementations portable across NVIDIA and AMD GPUs, delivering up to 70% faster inference compared to vanilla vLLM.

• Mojo Language provides Python-like syntax with systems-level performance, enabling developers to write high-performance GPU code without deep CUDA expertise while achieving speeds up to 12x faster than Python.

• Mammoth Orchestration scales AI workloads from a single GPU to unlimited nodes with a Kubernetes-native control plane specifically designed for large-scale distributed AI serving.

• Hardware Portability eliminates vendor lock-in through breakthrough compiler technology that automatically generates optimized kernels for any hardware target, supporting NVIDIA, AMD, and Apple Silicon.

• Tiny Containers delivers 90% smaller container sizes (under 700MB vs vLLM) with sub-second cold starts, reducing infrastructure costs and deployment complexity.

• Open Source Stack democratizes high-performance AI by open-sourcing the entire stack including optimized kernels, enabling full customization down to the silicon level.

• Enterprise Support includes SOC 2 Type I certification, dedicated engineering contacts, custom SLAs, and flexible deployment options including cloud, on-premise, and hybrid configurations.

To get started, install the free Community Edition via Docker, PIP, UV, PIXI, or Conda, then deploy GenAI models locally using the OpenAI-compatible API. Browse the model repository at builds.modular.com to find optimized models for your use case.