Breaking the Bandwidth Wall: Why AI Clusters are Shifting to Ultra Ethernet
To scale past 100k GPUs, the industry is replacing proprietary InfiniBand with AI-optimized Ultra Ethernet.
AI Infrastructure
Multi-Cloud GPU Arbitrage: Routing Workloads Between Hyperscalers and Neoclouds
Don't lock into one vendor. Learn how to use an abstraction layer to route training and inference workloads to the cheapest available capacity across hyperscalers and neoclouds.
Semantic Caching at Scale: Vector Embeddings for 5x Latency Reduction
Moving beyond exact-match caching for repetitive zero-shot inference workloads. Learn how to architect semantic caching to slash latency and compute costs.
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.
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.