Rack-Scale AI Design: The End of Component Scaling
We have hit the physical limits of what a single chip can do. The new unit of compute for AI infrastructure isn't the GPU; it's the fully integrated rack.
AI Infrastructure
TTFT vs ITL: The Two Metrics Defining Inference Performance
Average latency is a lie that hides tail-end failures. To truly optimize AI inference in 2026, you must separate your Time To First Token from your Inter-Token Latency.
Hierarchical KV Caching: Scaling Context Beyond VRAM Limits
As context windows scale to a million tokens, the KV cache becomes too large for GPU memory. The solution is a multi-tiered cache that offloads data to CPU and NVMe without killing latency.
xAI Model Training Infrastructure: The Grok Tech Stack
The xAI model training infrastructure for Grok relies heavily on JAX and Rust. Discover the architecture, hardware stack, and how xAI scales their massive GPU clusters without PyTorch.
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.