7 citations · 7 across the 11 of their papers we have counts for
7 papers · 1 filter
Hot-Cold Tiering of HBM and High Bandwidth Flash for Agentic LLM Serving
Jongjin Baek, Won Ji, Seungjae Yoo +1
Large language model (LLM) serving is increasingly agentic, with multi-turn sessions that idle between actions yet must retain their full context. Limited GPU memory capacity force…
APT: Accelerating Diffusion Transformers via Attention Probability-Guided Pruning and Quantization
Sungyeob Yoo, Seeyeon Kim, Joonyong Park +2
Recent advances in generative AI have significantly increased the demand for high-resolution image and video generation, positioning diffusion models as a core technology. Among th…
Beyond Capacity: Scalable MoE LLM Inference via High-Bandwidth Flash with Direct GPU and HBM Paths
Seeyeon Kim, Juhyeong Jin, Joo-Young Kim
Modern mixture-of-experts (MoE) language models increasingly strain the capacity and cost efficiency of high-bandwidth memory (HBM), as rapidly growing expert weights must be provi…
Cassandra: Enabling Reasoning LLMs at Edge via Self-Speculative Decoding
Soongyu Choi, Yuntae Kim, Muyoung Son +1
Speculative decoding has emerged as a promising lossless approach for accelerating Large Language Models (LLMs). As reasoning LLMs increasingly suffer from decode-stage overhead an…
DiSC: Resolution-Scalable Acceleration of Diffusion Models by Exploiting Sparsity and Cached Token Reuse with Hash-based Distribution
Jieon Yoon, Hangyeol Lee, Jaehoon Heo +1
Transformer-based diffusion models offer superior scalability and performance but suffer from high computational overhead due to the iterative nature and quadratic complexity of se…
MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization
Seeyeon Kim, Jaehun Lee, Sungyeob Yoo +1
Masked diffusion enables region-specific image synthesis but suffers from computational redundancy, since the entire image is processed each timestep even though only the masked re…
ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput
Junsoo Kim, Hunjong Lee, Geonwoo Ko +4
The growing adoption of Large Language Models (LLMs) across various domains has driven the demand for efficient and scalable AI-serving solutions. Deploying LLMs requires optimizat…