The article explains why a single GPU can show multiple compute figures, clarifies the meanings of FLOPS, TOPS, precision levels and structured sparsity, demonstrates calculations with RTX 5090 and H100, and provides practical guidance for interpreting theoretical peaks versus real‑world performance.
In a deep interview, former Google TPU architect Reiner Pope explains that low‑concurrency fast‑mode services trade higher fees for faster streaming but are limited by memory‑bandwidth bottlenecks, that optimal concurrency balances compute and memory costs, and that pipeline‑parallel sparse expert models and reinforcement‑learning fine‑tuning introduce new inefficiencies and overtraining risks.
This article presents a hardware‑aware co‑design framework for edge‑deployed large language models, revealing a scaling law that balances model accuracy and inference latency, and demonstrates how Pareto‑optimal architectures can be discovered quickly using roofline analysis and systematic search on devices like NVIDIA Jetson Orin.
This article breaks down the hardware bottlenecks of large‑scale LLM training, explains the Roofline performance model, arithmetic intensity, and how computation and communication costs interact on GPUs and TPUs, offering concrete formulas and examples for efficient scaling.
