Showing cs.ARShow all
2 papers · 1 filter
cs.AR2026
Rethinking LLM Inference Bottlenecks: Insights from Latent Attention and Mixture-of-Experts
Sungmin Yun, Seonyong Park, Hwayong Nam +10
Computational workloads composing traditional transformer models are starkly bifurcated. Multi-Head Attention (MHA) and Grouped-Query Attention are memory-bound due to low arithmet…
cs.AR2024
Duplex: A Device for Large Language Models with Mixture of Experts, Grouped Query Attention, and Continuous Batching
Sungmin Yun, Kwanhee Kyung, Juhwan Cho +6
Large language models (LLMs) have emerged due to their capability to generate high-quality content across diverse contexts. To reduce their explosively increasing demands for compu…