Organizations Using XLA for ML Model Compilation, Optimization, and Acceleration
Discover organizations tagged with xla that leverage the XLA (Accelerated Linear Algebra) compiler to compile and optimize TensorFlow and JAX models for TPU and GPU acceleration; this curated list surfaces teams using XLA for kernel fusion, graph-level optimizations, quantization-aware compilation, and inference latency reduction. Explore actionable insights on how each organization applies XLA in production ML pipelines — from training speedups and memory footprint reduction to efficient model deployment and edge inference — with long-tail filters for framework (TensorFlow, JAX), hardware (TPU, GPU), and use case (training, inference, edge). Use the filtering UI to narrow results by technology, benchmark, or integration pattern, then view organization profiles and technical notes to evaluate measured performance gains and implementation patterns. Start filtering now to find organizations that implement XLA-driven model optimization and accelerate your ML R&D or vendor selection.
