xla

XLA is the machine learning compiler that takes models from JAX, PyTorch, or TensorFlow and optimizes them for GPUs, CPUs, TPUs, and other accelerators. If you use any of those frameworks you're already running XLA, you just don't see it. The OpenXLA repo is where the compiler itself lives, and it's open source under Apache 2.0.

Self-hosting in the normal sense doesn't apply. XLA is infrastructure that ML frameworks call into. End developers interact with it through `jax.jit`, `torch.compile` (with the OpenXLA backend), or TensorFlow's XLA flags. Building from source is a serious commitment that involves Bazel, platform-specific dependencies, and several days of patience. Most teams should consume the pre-built versions their framework ships.

Use this directly if you're doing ML infrastructure work: writing a new framework, adding hardware support, or debugging compilation issues. Solo developers and small teams: you don't interact with XLA directly, you benefit from it. Large teams doing custom silicon or model serving optimization: this is the layer you're working in.

The catch: governance still effectively sits with Google despite the OpenXLA branding. The Code of Conduct points back to TensorFlow governance, and Google contributes the majority of the changes. Apache 2.0 protects against the worst outcomes, but "open governance" reads aspirationally here.