45 citations · 105 across the 21 of their papers we have counts for
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cs.LG2025
Tequila: Trapping-free Ternary Quantization for Large Language Models
Hong Huang, Decheng Wu, Rui Cen +7
Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication t…
cs.DC2025
Learning In Chaos: Efficient Autoscaling and Self-Healing for Multi-Party Distributed Training
Wenjiao Feng, Rongxing Xiao, Zonghang Li +6
Node and link churn in multi-party, cross-region clusters over wide-area networks (WANs) often disrupts distributed training. However, checkpoint-based recovery and cloud-centric a…
cs.DC2025★ 1 cited
Prima.cpp: Fast 30-70B LLM Inference on Heterogeneous and Low-Resource Home Clusters
Zonghang Li, Tao Li, Wenjiao Feng +8
On-device inference offers privacy, offline use, and instant response, but consumer hardware restricts large language models (LLMs) to low throughput and capability. To overcome th…