A field guide to performance and cost engineering: claim-checking with the scaling and queueing laws, measurement discipline, where GPU and CPU budgets leak, and how to turn a benchmark into a hardware decision.
Qwen3 reports a roughly tenfold GPU-hour difference in one post-training comparison. The result explains the value of teacher feedback, not a universal rule for total frontier-model development cost.
I used whisper.cpp on an RTX 5070 to transcribe about 100 hours of podcasts in roughly three hours, then turned the Markdown output into a searchable prompt knowledge base.
GPU cost optimization starts with useful work, latency and paid capacity. A measurement-led review of data stalls, inference sizing, sharing and cost attribution separates removable spend from capacity gains.
GPUs are no longer the bottleneck. Data movement is. The bottleneck in AI infrastructure has moved from compute to data movement. The claim was articulated in an online talk I attended recently, and it matches what I have seen for years across very different systems: raw compute stops being the Read more