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

VentureBeat
VentureBeat · 03/16/2026 20:00 EDT

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model itself. The method, called KV Cache Transform Coding (KVTC), applies ideas from media compression formats like JPEG to shrink the key-value cache behind multi-turn AI systems, lowering GPU memory demands and speeding up time-to-first-token by up to 8x.For enterprise AI applications that rely on agents and long.

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