[12][13] MLIR is a newer compiler framework that allows Mojo to exploit higher level compiler passes unavailable in LLVM alone, and allows Mojo to compile down and target more than only central processing units (CPUs), including producing code that can run on graphics processing units (GPUs), Tensor Processing Units (TPUs), application-specific integrated circuits (ASICs) and other accelerators.
It can also often more effectively use certain types of CPU optimizations directly, like single instruction, multiple data (SIMD) with minor intervention by a developer, as occurs in many other languages.
[17] Intention behind Mojo is to bridge the gap between Python’s ease of use and the fast performance required for cutting-edge AI applications.
[22] In March of 2024, Modular open sourced the Mojo standard library and started accepting community contributions under the Apache 2.0 license.
The language is not source-compatible with Python 3, only providing a subset of its syntax, e.g. missing the global keyword, list and dictionary comprehensions, and support for classes.
Further, Mojo also adds features that enable performant low-level programming: fn for creating typed, compiled functions and "struct" for memory-optimized alternatives to classes.