How to optimize LLM inference speed and reduce costs in production

This post details techniques to optimize LLM inference speed and reduce costs in production, including continuous batching, speculative decoding (draft-target, Medusa, EAGLE-3, N-gram speculation), KV cache optimizations (KV cache-aware routing, CPU offloading), quantization (mixed-precision, rotation-based, curvature-based), kernel optimizations (kernel fusion, attention kernels, asynchronous compute, PDL), intelligent request routing (geo-aware load balancing, LoRA-aware routing), and topology-aware parallelism (Tensor Parallelism, Expert Parallelism).