The Kubernetes for AI Paradigm
Native K8s orchestration is evolving to handle GPU scheduling, checkpointing, and live migration at the scale that AI demands.
Tag: Infrastructure
Scaling Vector Databases for High-Throughput Text Clustering
Analyzing the bottleneck of bulk clustering and using exact-match caching to reduce index compute load.
How xAI Built Grok: JAX, Rust, and the GPU Training Infrastructure
How xAI built Grok without PyTorch: Discover the JAX and Rust training infrastructure, GPU cluster architecture, and hardware stack powering Grok.
Demystifying Google TPU SparseCore: Accelerating Recommendation Systems
How Google TPU SparseCore solves embedding lookup bottlenecks in recommender models. Learn the co-designed architecture of Trillium's SparseCores.
The Real Performance Improvement Rate of AI Training Chips
Analyze the actual performance improvement rate of training chips and GPUs vs marketing hype. Here is the data on real compute scaling for training and inference.
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.