Scaling Recommendations with TPU SparseCore
TPU SparseCore: How specialized silicon solves the massive memory bottleneck of embedding lookups in large-scale recommendation models.
AI Infrastructure
Speculative Decoding Infrastructure: Squeezing Latency without Hardware Upgrades
The bottleneck for LLMs is memory bandwidth, not compute. Discover how to use speculative decoding on GCP to achieve 3x speedups by using small "draft" models to accelerate massive "oracle" models.
HBM-Aware Load Balancing with libtpu and GKE
CPU load is a trailing indicator for AI inference. Discover how to use libtpu metrics and the GKE Gateway API to build high-density, memory-aware traffic routing for TPUs.
Beyond Vibe-Checks: Trajectory Evaluation & Synthetic Adversaries
Is your agent actually reasoning, or just lucky? Discover why trajectory analysis and synthetic red-teaming are the only ways to build production-grade autonomous systems.
Stateful Agents on K8s: Redis is Your Bottleneck, Not the Vector DB
Agents are stateless. Their memory is not. Scaling the LLM reasoning loop is trivial compared to solving the transactional concurrency of agent memory on Kubernetes.
Writing Pallas Kernels for JAX: Stepping Outside the XLA Safety Net
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.