Tag: JAX

The AI industry is shifting from celebrating large compute budgets to hunting for efficiency. Your competitive advantage is no longer your GPU count, but your cost-per-inference.

Nvidia Blackwell microscaling and the new FP4 formats double inference speeds. Dive into how the second-generation Transformer Engine uses scale factors and sparsity for AI workloads.

Business case for JAX in AI training: compare JAX vs custom C++ training stack performance. See how compiler-first JAX reduces data movement overhead and improves throughput by 2.7x.

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.