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.
OpenAI and Databricks have forged a multiyear $100 million partnership to embed powerful AI agents into enterprise data platforms, enabling organizations to build agents using their own data.
Google is integrating Gemini AI into Chrome for U.S. users, bringing features like multi-tab summarization, agentic task automation, and deeper sync with Google apps.
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.
IBM has unveiled a detailed development plan for Quantum Starling, the world’s first large-scale fault-tolerant quantum computer, aiming for deployment by 2029 – setting a milestone for practical quantum capability.