KV Cache

KV Cache (Key-Value Cache) stores the intermediate attention computations from previously-processed tokens so that LLM inference does not recompute them at every generation step. Without KV caching, generating each new token would require processing the entire prompt and previous output from scratch — making conversation impractical. The KV cache grows linearly with sequence length and can dominate GPU memory for long contexts; for a 70B-parameter model with 32K context, the KV cache alone consumes tens of GB. PagedAttention (introduced in vLLM) revolutionized KV cache management by treating memory like virtual memory in operating systems, enabling dramatic throughput improvements. Other optimizations include multi-query attention (MQA), grouped-query attention (GQA, used in Llama 3 and onward), and KV cache quantization. AI governance, AI compliance, and AI risk management programs document architecture choices in technical evidence supporting responsible AI reproducibility across long-context enterprise AI inference deployments.

Centralpoint Lets You Scale Long-Context AI Safely: Oxcyon's Centralpoint AI Governance Platform handles long-context model serving — Claude 200K, Gemini 1M, GPT-4 128K — alongside on-prem Llama and embedded models. Centralpoint meters consumption, keeps prompts and skills on-prem, and embeds chatbots into your portals via one JavaScript line.


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