The key-value cache holds intermediate attention results for tokens already processed, letting generation continue cheaply token by token. It speeds output but grows with context length and concurrency, making it a main driver of serving memory and a target for optimisation like PagedAttention.
The book · Volume 7 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Real-Time Pipelines and Agents to Serving, Cost, Compliance, and Verticals
The KV cache is often what runs a GPU out of memory when context grows or users pile in, long before compute runs out. This volume covers the KV cache in serving: vLLM and PagedAttention, Triton and TensorRT, batching and concurrency, and the cost per stream-hour that memory limits drive. Written by Nikolay Sapunov, CEO at Fora Soft.
Running out of GPU memory because the KV cache grows with traffic?
Share your model, context lengths and concurrency, and we will tune KV cache and serving settings and report the throughput and cost gains. 250+ video products shipped since 2005.
