20 citations · 21 across the 9 of their papers we have counts for
5 papers · 1 filter
SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving
Gunho Park, Kyoungho Jeun, Juntaek Oh +3
Mixture-of-experts (MoE) models activate few experts per token, yet batched decoding can access nearly the entire expert pool, making expert-weight traffic a major bottleneck. Expe…
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…
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…