arXiv: cs.AI preprintsInternational6 November 2026
GAAC: Lossless KV Compression for Bandwidth-Efficient LLM Inference on Personal Devices
This is an official announcement record
Firsthand records what arXiv: cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2610.38210v2 Announce Type: replace Abstract: Weight-only, lossy quantization substantially reduces the storage and memory requirements for serving large language models (LLMs), but the growing key-value (KV) cache remains a bottleneck for long-context inference on personal devices. We present Group-Adaptive Additive Codec (GAAC), a lossless KV compression and serving system that exploits exponent redundancy to reduce storage and accelerate attention without introducing KV approximation. GAAC encodes BF16 exponents using calibrated per-channel base-plus-offset codes over contiguous range
Read the official announcement
Opens arxiv.org
More from arXiv: cs.AI preprints
- Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation6 November 2026
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks6 November 2026
- BlockLock: Reproducible LLM Inference with Fixed Batch Geometry6 November 2026
- Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism6 November 2026
- The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record6 November 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.