Why build this? MATLAB wasn’t chosen by engineers; it was inherited from classrooms. That unfair advantage let MathWorks sell a decades-old runtime with heavy startup, sluggish hot loops, and paywalled toolboxes. It milks about $500M a year out of the engineering ecosystem for what amounts to an outdated compiler and runtime stack.

GNU Octave has been the main open-source way to run MATLAB code but it only supports a subset of the grammar and semantics, and its performance is far behind modern expectations. It’s more of a compatibility bridge than a true runtime alternative.

I decided to implement a MATLAB language compiler and executor from scratch, grammar and semantics complete, in Rust with a modern architecture inspired by V8. Like V8, RunMat starts in a lightweight interpreter, then profiles and JITs hot paths using Cranelift. Snapshotting makes cold start essentially vanish (5ms vs 2–10s in MATLAB), and tensor operations run natively across CPUs or GPUs (CUDA, Metal, Vulkan) without an extra license.

Performance (vs Octave, Apple M2 Max, 32GB): * Startup: 0.9147s → 0.005s (180× faster) * Matrix ops: 0.8220s → 0.005s (164×) * Math funcs: 0.8677s → 0.0053s (160×) * Control flow: 0.8757s → 0.0057s (154×)

Unlike Octave, RunMat implements the full MATLAB grammar: arrays/indexing (end/colon/masks/N-D), multiple returns, varargin/varargout, classdef OOP, events/handles, metaclass, and standardized exceptions. The core is slim at 5MB static binaries for Linux, macOS, and Windows (or embedded devices and containers), with language extensibility coming from packages that can be written in MATLAB or in Rust.

It’s 100% open source: The repo is about 3M characters of code, has over 1,000 tests covering the edges of the semantic surface, and was bootstrapped in three weeks. A package manager is next, along with a final draft of the builtin standard library.

TLDR: Same language semantics, but rebuilt with modern methods, the way Chrome’s V8 redefined JavaScript engines.