Research & Tools

Google Research shrinks LLM working memory up to 6x

Google Research published TurboQuant to compress large language model key-value caches. Early tests show up to 6x reduction in KV cache memory and up to 8x faster inference with no measurable accuracy loss. The method pairs PolarQuant coordinate conversion with a Quantized Johnson‑Lindenstrauss residual step.

1 source · Mar 25

Timeline

• Mar 25 Reported by Help Net Security

Sources

• Mar 25 Help Net Security

  Google's TurboQuant cuts AI memory use without losing accuracy - Help Net Security

  Google's TurboQuant uses AI model compression to cut memory use by 6x and boost inference speed 8x without any loss in accuracy.

  original

Part of the PlainSec briefing for 2026-03-26

Every edition of this story: Google Research shrinks LLM working memory up to 6x