most citedAny-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

4 citations · 4 across the 1 of their papers we have counts for

collaborators

2 papers

cs.LG2026

LoopSpec: Pipelined Self-Speculative Decoding for Looped Transformers

SangLyul Cho, Langqing Cui, Sehoon Kim +2

Looped Transformers achieve strong performance with compact parameter sizes by repeatedly applying a shared stack of Transformer blocks across recurrent depths. However, they incur…

cs.LG2024★ 4 cited

Any-Precision LLM: Low-Cost Deployment of Multiple, Different-Sized LLMs

Yeonhong Park, Jake Hyun, SangLyul Cho +2

Recently, considerable efforts have been directed towards compressing Large Language Models (LLMs), which showcase groundbreaking capabilities across diverse applications but entai…