Woyce
A practical breakdown of how GPUs, TPUs, and custom AI ASICs differ in architecture, cost, and performance, and how to think about picking between them.
A plain-language explanation of high bandwidth memory (HBM), why it has become the tightest bottleneck in AI hardware, and what that means for anyone building or buying AI infrastructure.
A plain explanation of wafer-scale computing — why chipmakers stopped cutting wafers into individual dies and started building processors the size of dinner plates.
A look at how specialized chips like LPUs and transformer ASICs are challenging GPU dominance in AI inference, and what that shift means for teams deploying models in production.
Co-packaged optics move light-based interconnects next to the switch silicon itself, cutting the power and latency costs that pluggable transceivers impose on AI datacenter networks.
A plain explanation of how GPUs inside a server and servers inside a cluster exchange data during AI training, and why the network is now the bottleneck.
A look at why air cooling is running out of road for AI-era server racks, and how liquid cooling technologies are stepping in to handle 100kW-plus densities.
A breakdown of what actually drives the cost of running large language models in production, from prefill and decode to KV cache memory and GPU utilization.
A plain-language guide to how FP8, FP4, and INT4 quantization make AI models faster and cheaper to run, and where the trade-offs bite.
A look at how AI compute demand is shifting from training massive models to running them at scale, and why that shift changes cost, hardware, and infrastructure decisions.
A practical explainer on quantum error correction — how physical qubits combine into logical qubits, why the error threshold matters, and what it means for real quantum computing timelines.
Physical AI is the term for AI systems that perceive, reason, and act in the physical world through robots and machines rather than just generating text or images.