LLM Inference Optimization

Why Inference Is Slow

LLM inference is autoregressive — each token generation requires recomputing attention over the entire sequence. This leads to quadratic computation growth with sequence length.

KV Cache

Cache previously computed Key and Value matrices. New tokens only need incremental computation.

Flash Attention

Through tiled computation and IO optimization, Flash Attention minimizes memory access for attention calculation. Flash Attention 2 is 2-3x faster than standard implementations.

Quantization

Convert FP16 models to INT8 or INT4, significantly reducing VRAM usage and latency. GGUF and AWQ are common quantization formats.

Understanding Self-Attention computation is the foundation for inference optimization — see Transformer Attention Mechanism Explained.