Can Mojo Make Python 35,000× Faster? Inside the New AI‑Focused Language

Modular, a rising AI‑tech startup, has launched Mojo, a new programming language that aims to combine Python’s ease of use with C‑level speed.

Mojo differentiates itself from other Python‑accelerating projects such as JAX, Codon, or Julia by leveraging hardware acceleration; in benchmarks it runs the Mandelbrot algorithm up to 35,000 times faster than native Python.

The language was created by Chris Lattner—former Apple, Google and Tesla senior engineer who co‑authored LLVM, MLIR and Swift—together with Tim Davis, a former Google ML team leader.

Mojo promises a Python‑compatible core while adding system‑programming features from C, C++ and CUDA, zero‑cost abstractions, Rust‑like memory safety, and compile‑time metaprogramming.

Through MLIR, Mojo code can tap AI‑specific hardware such as TensorCores and AMX extensions, giving it a performance edge for certain algorithms; on an AWS r7iz.metal‑16xl instance, a Mandelbrot test completed in 0.03 s versus 1,027 s for Python 3.10.9.

Current status

Mojo is still under development, but a Jupyter notebook is available for developers to experiment with.

When fully released, Mojo is expected to become a Python superset with a system‑programming toolkit, eventually supporting the full Python ecosystem. It already implements core Python features such as async/await, error handling and variadic arguments, though full compatibility remains work in progress.

Fast.ai founder Jeremy Howard called Mojo “the biggest programming‑language advancement in decades,” noting that it addresses the “bilingual” reality of AI development where Python code must often be linked to high‑performance modules written in C/C++ or Rust.

Howard highlighted that developers can switch to a faster “fn” mode, explicitly typing variables so Mojo can generate optimized machine code, and that using struct instead of class packs data tightly in memory, offering C‑like speed with minimal new syntax.

Mojo can also compile programs into standalone, fast‑starting binaries, simplifying deployment on available cores and accelerators.

Remaining gaps include package management and a build system, challenges the broader Python community is also tackling.

Mojo is not yet open‑source, but an open‑source release is expected in the future.