most citedMoSKA: Mixture of Shared KV Attention for Efficient Long-Sequence LLM Inference

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cs.AR2026

StreamDQ: Near-Memory Weight DeQuantization in Custom HBM for Scalable AI Inference Acceleration

Minki Jeong, Daegun Yoon, Soohong Ahn +7

As large language models (LLMs) scale, their memory and computation demands have grown substantially, making weight-only quantization a widely adopted technique for reducing model…

cs.AR2025

HPU: High-Bandwidth Processing Unit for Scalable, Cost-effective LLM Inference via GPU Co-processing

Myunghyun Rhee, Joonseop Sim, Taeyoung Ahn +6

The attention layer, a core component of Transformer-based LLMs, brings out inefficiencies in current GPU systems due to its low operational intensity and the substantial memory re…