KernelSight-LM: A Kernel-Level LLM Inference Simulator
As large language models (LLMs) move into production serving, practitioners must rapidly evaluate inference performance across diverse hardware, models, and serving parameters to meet cost and latency targets. However, the end-to-end behavior of LLMs couples serving-layer policies with low-level GPU kernel execution and rapidly evolving architectures, forcing slow, deployment-specific benchmarking that is hard to generalize. We present KernelSight-LM, a fine-grained inference simulator that models token-level execution and produces kernel-level latency breakdowns. It decomposes each serving step into a roofline kernel model with a learned efficiency term, a communication model, and a host...
| Authors | Xiteng Yao, Taeho Kim, Hengzhi Pei et al. |
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| Primary category | cs.PF |
| Published | 26 June 2026 |
| Last updated at source | 2 July 2026 |
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