2 papers
cs.LG2026
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…
cs.LG2026
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…