21 citations · 103 across the 22 of their papers we have counts for
22 papers
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…
Unifying Uniform and Binary-coding Quantization for Accurate Compression of Large Language Models
Seungcheol Park, Jeongin Bae, Beomseok Kwon +5
How can we quantize large language models while preserving accuracy? Quantization is essential for deploying large language models (LLMs) efficiently. Binary-coding quantization (B…
Faster Inference of LLMs using FP8 on the Intel Gaudi
Joonhyung Lee, Shmulik Markovich-Golan, Daniel Ohayon +9
Low-precision data types are essential in modern neural networks during both training and inference as they enhance throughput and computational capacity by better exploiting avail…
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…