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20232026
most citedFlattenQuant: Breaking Through the Inference Compute-bound for Large Language Models with Per-tensor Quantization

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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

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…