7 citations · 10 across the 9 of their papers we have counts for
5 papers · 1 filter
CodeGEMM: A Codebook-Centric Approach to Efficient GEMM in Quantized LLMs
Gunho Park, Jeongin Bae, Byeongwook Kim +5
Weight-only quantization is widely used to mitigate the memory-bound nature of LLM inference. Codebook-based methods extend this trend by achieving strong accuracy in the extremely…
AnyBCQ: Hardware Efficient Flexible Binary-Coded Quantization for Multi-Precision LLMs
Gunho Park, Jeongin Bae, Beomseok Kwon +3
The deployment of large language models (LLMs) is increasingly constrained by memory and latency bottlenecks, motivating the need for quantization techniques that flexibly balance…
To FP8 and Back Again: Quantifying Reduced Precision Effects on LLM Training Stability
Joonhyung Lee, Jeongin Bae, Byeongwook Kim +2
The massive computational costs associated with large language model (LLM) pretraining have spurred great interest in reduced-precision floating-point representations to accelerate…
No Token Left Behind: Reliable KV Cache Compression via Importance-Aware Mixed Precision Quantization
June Yong Yang, Byeongwook Kim, Jeongin Bae +5
Key-Value (KV) Caching has become an essential technique for accelerating the inference speed and throughput of generative Large Language Models~(LLMs). However, the memory footpri…
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
Se Jung Kwon, Jeonghoon Kim, Jeongin Bae +7
There are growing interests in adapting large-scale language models using parameter-efficient fine-tuning methods. However, accelerating the model itself and achieving better infer…