Production & Ops
Inference Optimization
Making models fast and cheap
Inference optimization covers quantization (reducing precision), batching (processing multiple requests), KV caching (avoiding recomputation), and speculative decoding (using a small model to draft, a large model to verify). The goal: same quality, less compute.
Why this matters
84% of companies report AI costs hurt gross margins by 6+ points (Menlo). Inference optimization is the difference between a viable product and a money pit.