1 citations · 1 across the 7 of their papers we have counts for
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LiteCache: A Query Similarity-Driven, GPU-Centric KVCache Subsystem for Efficient LLM Inference
Jiawei Yi, Ping Gong, Youhui Bai +10
During LLM inference, KVCache memory usage grows linearly with sequence length and batch size and often exceeds GPU capacity. Recent proposals offload KV states to host memory and…
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
CentroidKV: Efficient Long-Context LLM Inference via KV Cache Clustering
Jie Hu, Shengnan Wang, Yutong He +8
Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for…
BigMac: A Communication-Efficient Mixture-of-Experts Model Structure for Fast Training and Inference
Zewen Jin, Shengnan Wang, Jiaan Zhu +5
The Mixture-of-Experts (MoE) structure scales the Transformer-based large language models (LLMs) and improves their performance with only the sub-linear increase in computation res…