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cs.LG2025
Identifying Sensitive Weights via Post-quantization Integral
Yuezhou Hu, Weiyu Huang, Zichen Liang +4
Serving Large Language Models (LLMs) is costly. However, post-training weight quantization can address this problem by both compressing their sizes for limited memory and saving ba…
cs.LG2025
SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors
Bohan Lyu, Siqiao Huang, Zichen Liang
Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks, su…
cs.LG2024
Beyond 2:4: exploring V:N:M sparsity for efficient transformer inference on GPUs
Kang Zhao, Tao Yuan, Han Bao +6
To date, 2:4 sparsity has stood as the only sparse pattern that can be accelerated using sparse tensor cores on GPUs. In practice, 2:4 sparsity often possesses low actual speedups…