1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
KernelClaw: A Multi-Agent Framework for GPU Kernel Optimization with Dual-Memory Mechanisms
Qitong Sun, Jun Han, Tianlin Li +6
Improving GPU kernel efficiency is crucial for efficient 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…
Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment
Fei Yang, Shuang Peng, Ning Sun +5
Large language models (LLMs) such as GPT-3, OPT, and LLaMA have demonstrated remarkable accuracy in a wide range of tasks. However, training these models can incur significant expe…
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…