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.
From the Front Lines of Tech
Sharing strategic insights and lessons from over 20 years of building scalable systems, leading high-performing teams, and navigating complex technology shifts.
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.
· StrategyThe P&L Mandate: Transitioning the CAIO from Pilots to Profitability
Boards demand hard financial ROI over soft metrics like 'hours saved'. This is the framework to shift your AI strategy toward measurable margin and revenue impact.
RadixAttention vs PagedAttention: The New Frontier in Context Management
A deep dive into the mechanics of SGLang's RadixAttention and why it represents a breakthrough for multi-turn agentic workflows compared to vLLM's PagedAttention.
The Infinite Board Problem: Pruning State in Long-Running Reasoning Loops
How to manage the shared state size in complex reasoning loops to prevent context window overflow without losing critical history.
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.