arxiv
PublishedJune 12, 2026 at 4:00 AM
Multi-Bitwidth Quantization for LLMs Using Additive Codebooks
Publisher summary· verbatim
arXiv:2606.12876v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively manage the trade-off between performance and efficiency without retraining is critical. We propose Dr
Stay posted· Newsletter
A 5-min weekly brief — top movers, price watch, story of the week.
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivLoHoSearch: Benchmarking Long-Horizon Search Agents Beyond the Human Difficulty Ceiling1harxivMulti-Turn Reasoning When Context Arrives in Pieces: Scalable Sharding and Memory-Augmented RL1harxivSkillChain: Closing the Loop on Skill Evolution for Image-Based E-Commerce AI Assistants1harxivNo Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions1hThe Bubble Brief
WEEKLYRead AI insights every Tuesday — top movers, new releases, story of the week.
Originally published on arxiv ↗