midLLMOps
What is latency optimization in LLM inference pipelines?
Updated May 16, 2026
Short answer
Latency optimization reduces response time in LLM systems through caching, batching, and model optimization.
Deep explanation
LLM inference latency comes from model size, token generation speed, and network overhead. Optimization techniques include KV caching, request batching, quantization, speculative decoding, and model distillation.
Real-world example
ChatGPT-style systems using streaming token generation to improve perceived latency.
Common mistakes
- Ignoring token generation cost per request.
Follow-up questions
- What is KV caching?
- How does batching improve throughput?