AI Infrastructure

In distributed training, the slowest packet determines the speed of the cluster. We benchmark GCP's 'Circuit Switched' Jupiter fabric against AWS's 'Multipath' SRD protocol.

As the AI industry moves from model training to large-scale deployment, the strategic bottleneck has shifted from parameter count to inference orchestration. This post explores how advanced techniques like RadixAttention, Chunked Prefills, and Deep Expert Parallelism are redefining the ROI of GPU clusters and creating a new standard for high-performance AI infrastructure.

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.

An end-to-end guide to orchestrating Custom Qwen3 pre-training on Google Cloud's Trillium TPUs. I dive into modifying the Qwen3 architecture for structured JSON outputs, leveraging XPK for orchestration, and serving the final artifacts with vLLM's high-performance openXLA backend.

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.

Google Cloud’s G4 architecture delivers 168% higher throughput by maximizing PCIe Gen 5 performance. This deep dive examines the engineering stack driving these gains, from direct P2P communication and NUMA optimizations to Titanium offloads. Explore how G4 transforms standard connectivity into a high-speed fabric for demanding AI inference and training.