1 citations · 1 across the 4 of their papers we have counts for
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BUDDY: BUdget-Driven DYnamic Depth Routing for Adaptive Large Language Model Inference
Yuhua Zhou, Shaoqi Yu, Shichao Weng +4
Large language models (LLMs) incur high inference cost due to their depth and parameter scale. Depth pruning can reduce latency by skipping redundant Transformer blocks, but existi…
KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization
Qitong Sun, Jun Han, Tianlin Li +6
Improving GPU kernel efficiency is crucial for advancing AI systems. Recent work has explored leveraging large language models (LLMs) for GPU kernel generation and optimization. Ho…
FlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization
Yi Zhang, Fei Yang, Shuang Peng +2
Large language models (LLMs) have demonstrated state-of-the-art performance across various tasks. However, the latency of inference and the large GPU memory consumption of LLMs res…
Exploring Post-Training Quantization of Protein Language Models
Shuang Peng, Fei Yang, Ning Sun +3
Recent advancements in unsupervised protein language models (ProteinLMs), like ESM-1b and ESM-2, have shown promise in different protein prediction tasks. However, these models fac…