5 citations · 9 across the 9 of their papers we have counts for
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cs.CR2024
PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption
Yifan Tan, Cheng Tan, Zeyu Mi +1
Confidential computing on GPUs, like NVIDIA H100, mitigates the security risks of outsourced Large Language Models (LLMs) by implementing strong isolation and data encryption. None…
cs.LG2024★ 1 cited
Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters
Yixin Song, Haotong Xie, Zhengyan Zhang +4
Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However…
cs.LG2024★ 5 cited
PowerInfer-2: Fast Large Language Model Inference on a Smartphone
Zhenliang Xue, Yixin Song, Zeyu Mi +3
Large language models (LLMs) on smartphones enable real-time AI assistance and privacy-preserving, offline operation. However, resource constraints of smartphones limit current dep…