Tag: XLA

When XLA's heuristics fail for custom attention mechanisms, you can't just hope for a compiler update. Here is how you write Triton-like kernels directly in Python using JAX Pallas.

Recompilation is the silent killer of training throughput. If you see 'Jit' in your profiler, you are losing money. We dive into XLA internals.

The competitive advantage in AI has shifted from raw GPU volume to architectural efficiency, as the "Memory Wall" proves traditional frameworks waste runtime on "data plumbing." This article explains how the compiler-first JAX AI Stack and its "Automated Megakernels" are solving this scaling crisis and enabling breakthroughs for companies like xAI and Character.ai.

As hardware lead times and power constraints hit a ceiling, the competitive advantage in AI has shifted from chip volume to architectural efficiency. This article explores how JAX, Pallas, and Megakernels are redefining Model FLOPs Utilization (MFU) and providing the hardware-agnostic Universal Adapter needed to escape vendor lock-in.