Latency Optimization

User types a prompt. Waits five seconds for a response. Gets frustrated. Leaves. That's a latency problem. Latency is perceived speed and it matters enormously for user experience. Optimization has multiple angles. Model-level: use smaller models (faster), distillation (train a smaller model to mimic a larger one, keeping speed gains with acceptable accuracy loss), quantization (use lower precision, 8-bit instead of 32-bit, faster and cheaper). Infrastructure-level: caching (store common queries and responses, serve from cache, near-instant), batching (group requests, process together, amortize overhead), parallelization (process requests in parallel), sharding (split work across machines). There's also the retrieval optimization angle. Retrieving context is slow. Approximate nearest neighbor search speeds it up (trading accuracy for speed). Reranking only top candidates (don't rerank everything, just the top 100). Caching retrieved documents (frequent queries hit cache). Latency budgets drive everything. Consumer-facing systems (sub-second responses) have tighter budgets than enterprise systems (multi-second acceptable). Your budget determines what optimizations are viable. Sub-100ms budget? You can't afford neural reranking, stick with heuristic ranking. Sub-500ms budget? You can afford some reranking. Sub-2 second? You can afford full end-to-end processing. The latency-accuracy tradeoff is real. Aggressive optimization might speed things up but hurt accuracy. Quantized models are faster but less accurate. Cached responses are faster but less fresh. So you need to balance. Synap's latency optimization infrastructure provides caching, batching, model serving optimizations, and infrastructure tuning to help developers hit their latency targets without sacrificing quality.