For the past decade, the artificial intelligence landscape followed a remarkably simple recipe: if you wanted better AI, you built larger models, fed them more data, and added more GPUs. The formula worked, and it worked spectacularly. Every new generation of hardware enabled larger neural networks, and larger neural networks broadened their reach. What began … 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
There is a growing architectural tension at the heart of modern AI infrastructure. The processors that enabled the deep learning revolution—graphics processing units (GPUs)—remain the dominant engines of large-scale training and inference. Yet the computational profile of frontier language models is evolving in ways that increasingly expose the structural assumptions embedded in GPU design. Memory … Read more
The saying “round pegs do not fit square holes” persists because it captures a deep engineering reality: inefficiency most often arises not from flawed components, but from misalignment between a system’s assumptions and the problem it is asked to solve. A square hole is not poorly made; it’s simply optimized for square pegs. Modern large … 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