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KV Cache - Page 15

A KV cache, or key-value cache, speeds up autoregressive transformer inference by storing attention information calculated for tokens that have already been processed. Without the cache, the model would recompute those keys and values each time it generates another token. Reusing them reduces repeated work and improves response latency, especially for long prompts and outputs. The tradeoff is memory: cache size grows with sequence length, batch size, layer count, and model dimensions. Production systems manage this cost with paging, quantization, eviction, prompt caching, and request scheduling. KV caching changes inference efficiency, not the model’s learned knowledge, and cached data must be protected when prompts contain sensitive information.

Nvidia Invests $6.5 Billion in Technology That Could Reshape AI Infrastructure
By • 3 mins read
AI & Machine Learning, Cloud & Infrastructure, News, Research & Innovation

Nvidia Invests $6.5 Billion in Technology That Could Reshape AI Infrastructure

By • 3 mins read

Nvidia has committed at least $6.5 billion to photonics companies in recent months as it seeks to overcome AI infrastructure bottlenecks. The investments target optical technologies that could reduce energy consumption and improve data transfer across future AI systems.