Author

Seungyong Lee

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Jul 2026

NELSSA: A GPU-PNM Heterogeneous System for Mixed-Length LLM Serving via Length-based Request Placement

NELSSA is presented, an LLM serving system that integrates GPUs with real-world Processing-near-Memory accelerator devices to efficiently support mixed-length workloads and improves decode throughput by up to 5.5x in tokens/sec and reduces P99 latency by up to 15x compared to GPU-only baselines.

Sookyung Choi, Seungyong Lee, Kangkyu Park et al. · arXiv.org · 0 citations

Preprint Jul 2026

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

StreamDQ is proposed, a lightweight architectural enhancement that enables on-the-fly dequantization in the memory subsystem for high-throughput, large-batch LLM inference and reduces latency and improves decode throughput for end-to-end LLM inference.

Minki Jeong, Daegun Yoon, Soohong Ahn et al. · 0 citations