Inference Optimization
AssessTechniques
Methods for reducing latency and compute cost when serving model outputs.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 1 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 5The New Stack·9/13/2026researchChip Huyen on lowering LLM inference costs without new hardware
Chip Huyen’s P99 conference talk focuses on reducing large language model inference costs by improving measurement, model behavior, and request handling rather than buying new hardware. She highlights metrics such as time to first token, time per output token, end-to-end latency, and goodput, and distinguishes between model optimizations and service optimizations for inference systems.