Nvidia says it can shrink LLM memory 20x without changing model weights

Nvidia's KV Cache Transform Coding (KVTC) compresses LLM key-value cache by 20x without model changes, cutting GPU memory costs and time-to-first-token by up to 8x for multi-turn AI applications.

New KV cache compaction technique cuts LLM memory 50x without accuracy loss

MIT researchers developed Attention Matching, a KV cache compaction technique that compresses LLM memory by 50x in seconds — ...
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Skimpy HBM Memory Opens Up The Way For AI Inference Memory Godbox

Generative AI is arguably the most complex application that humankind has ever created, and the math behind it is incredibly complex even if the results are simple enough to understand. GenAI also it ...
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Understanding cache placement

In a computer, the entire memory can be separated into different levels based on access time and capacity. Figure 1 shows different levels in the memory hierarchy. Smaller and faster memories are kept ...