21 citations · 103 across the 22 of their papers we have counts for
18 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…
An Inquiry into Datacenter TCO for LLM Inference with FP8
Jiwoo Kim, Joonhyung Lee, Gunho Park +4
As large language models (LLMs) continue to scale, the high power consumption of AI accelerators in datacenters presents significant challenges, substantially increasing the total…
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…
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward Propagation
Sunghyeon Woo, Baeseong Park, Byeongwook Kim +4
Large language models (LLMs) have achieved significant success across various domains. However, training these LLMs typically involves substantial memory and computational costs du…