How KV Caching Eliminates Redundant Math in LLM Token Generation

The Key-Value (KV) cache is a core optimization in Large Language Model inference that avoids recomputing attention projections for previously seen tokens. In autoregressive generation, each new token must attend to all prior tokens, making repeated recalculation of Keys and Values computationally wasteful. The KV cache stores these vectors in memory so only the newest token's projections need to be computed at each step. This trades compute efficiency for memory bandwidth, shifting the bottleneck from FLOPs to VRAM, where the cache competes directly with model weights. Understanding the cache's size and behavior is essential for estimating serving costs, batch sizes, and overall inference throughput.