# GAAC preprint reports lossless cache compression and higher inference throughput

_Published Saturday, October 31, 2026 at 8:05 PM EDT · AI, Science · Latest · Tier 2 — Notable_

Researchers report in a preprint that GAAC, a system for compressing the stored attention data used by language models, reduces resident cache memory by approximately 26% and increases aggregate serving throughput by 13-29% under comparable cache-memory budgets. The system preserves cached values bit-for-bit.

GAAC compresses redundant exponent information and reconstructs data inside the attention kernel, avoiding full-cache decompression. Across five tested models, it achieved 1.405-1.407× compression of BF16 caches. On NVIDIA DGX Spark, end-to-end W4A16 decode steps accelerated by 1.04-1.15× at a 64K-token context; the larger 1.19-1.27× gains applied specifically to decode attention.

## Sources

- [cs.AI updates on arXiv.org](https://arxiv.org/abs/2610.38210)

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Canonical: https://techandbusiness.org/newswire/bfsiXZoSOyHtqeKFDH9OUc
Published: 2026-11-01T00:05:50.864Z
Story chronology: 2026-10-31T04:00:00.000Z
Retrieved: 2026-11-01T02:51:12.919Z
Publisher: Tech & Business (techandbusiness.org)