There are two major workloads that make up AI compute: training and inference. Training looks back. It digests a frozen body of past text, images and code, and distills it into weights. Inference looks forward. It takes those weights and, one token at a time, produces something that did not exist a moment ago. Same … Read more
Recent frontier LLM inference benchmarks have highlighted a recurring pattern. GPU-based systems deliver outstanding throughput when latency is not a concern, but their performance drops sharply once real-time response requirements are imposed. This behavior is sometimes attributed to software inefficiencies or suboptimal system tuning. In reality, the root cause lies much deeper. It reflects a … Read more
The AI industry has converged on a deceptively simple metric: cost per token. It’s easy to understand, easy to compare, and easy to market. Every new system promises to drive it lower. Charts show steady declines, sometimes dramatic ones, reinforcing the impression that AI inference is rapidly becoming cheaper and more efficient. But simplicity, in … Read more
Walk into any modern AI lab, data center, or autonomous vehicle development environment, and you’ll hear engineers talk endlessly about FLOPS, TOPS, sparsity, quantization, and model scaling laws. Those metrics dominate headlines and product datasheets. If you spend time with the people actually building or optimizing these systems, a different truth emerges: Raw arithmetic capability … Read more
From 2005 to 2017—the pre-AI era—the electricity flowing into U.S. data centers remained remarkably stable. This was true despite the explosive demand for cloud-based services. Social networks such as Facebook, Netflix, real-time collaboration tools, online commerce, and the mobile-app ecosystem all grew at unprecedented rates. Yet continual improvements in server efficiency kept total energy consumption … Read more