What is Inference Optimization?
TL;DR
The bundle of techniques — quantization, distillation, KV-cache, speculative decoding — that cut LLM inference cost, latency, and GPU memory in production.
Inference Optimization: Definition & Explanation
Inference Optimization is the umbrella for techniques that reduce inference cost, raise throughput, and trim latency and GPU memory in production LLM deployments. It pairs with training-side optimization as one half of LLMOps. Key methods: (1) Quantization (INT8/INT4 → 4-8x memory reduction), (2) Distillation (Haiku/Flash-class small models), (3) Speculative Decoding (a small model proposes, the big model verifies → 2-3x speedup), (4) KV-Cache optimization (PagedAttention, vLLM → 10x concurrency), (5) Continuous batching, (6) FlashAttention, (7) Tensor/Pipeline parallelism, (8) Model compilation (TensorRT-LLM, llama.cpp, MLX), and (9) batching engines (vLLM, TGI, SGLang). OpenAI, Anthropic, and Google rebuild internal stacks on these and have cut API prices 30-50% per year. Edge: llama.cpp, MLX (Apple Silicon), ONNX Runtime, OpenVINO. By 2026 Phi-4, Gemma 3, and Llama 4 8B run locally on M2 MacBooks and high-end phones thanks to combined quantization and distillation — fueling the local-LLM revolution. Inference is 70-80% of cost in most LLM apps, so optimization here decides whether the business is viable.