Chip Huyen explains how to cut inference costs without new hardware

The New Stack · 1 month ago · 16 ● 4 sources

Chip Huyen delivered and then recapped guidance on lowering large language model inference costs by measuring latency and improving batching, caching, and model/service efficiency rather than relying on new hardware. She cites caching hit rates of 90% to 97% from her open-source tool Sniffly. As a result, teams are increasingly prioritizing metrics like TTFT and goodput and using widely available techniques like prompt caching, quantization, and continuous batching to cut per-token costs while preserving model quality.