modular

MAX (Modular Accelerated Xecution) is a high-performance inference engine that compiles and optimizes AI models for diverse hardware targets (NVIDIA GPUs, AMD GPUs, Intel CPUs, Apple Silicon) from a single model representation, achieving throughput competitive with or exceeding framework-specific optimized runtimes without requiring hardware-specific code.

Mojo is Modular's new programming language designed to close the performance gap between Python (productive but slow) and C/C++ (fast but complex) specifically for AI and systems programming.

It is syntactically compatible with Python, enabling gradual adoption without rewriting existing Python codebases, but compiles to native machine code with SIMD vectorization, zero-cost abstractions, and memory model control that enables performance matching hand-optimized Caddressing the 'two-language problem' in AI systems programming where model logic is written in Python but performance-critical kernels require C++ or CUDA.

ML platform engineers seeking faster inference for production LLM and vision model serving, AI infrastructure teams evaluating alternatives to TensorRT or ONNX Runtime for multi-hardware deployment, and systems programmers building high-performance AI tooling use the Modular platform.

Mojo has attracted particular attention from the AI research community as a potential successor to the Python-for-prototyping, C++-for-performance workflowwith the promise that a single language and codebase could serve both needs without the friction of the current two-language approach that dominates production AI engineering.