How do you design inference optimization strategies for LLM serving?

Inference optimization uses batching, KV caching, quantization, and speculative decoding to improve latency and throughput.

LLM inference is expensive due to large matrix operations and autoregressive decoding. Optimization techniques include dynamic batching for GPU efficiency, KV caching to reuse attention states, model quantization to reduce memory footprint, and speculative decoding where smaller models predict tokens before verification by larger models.