arXiv:2606.28565v1 Announce Type: cross Abstract: 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
As LLMs scale and move into production, the need for efficient and predictable inference performance becomes critical to manage costs and meet user demand.
This simulator addresses a key bottleneck in LLM deployment by enabling rapid, accurate evaluation of inference performance across diverse hardware and models, which is crucial for optimizing cost and latency.
The ability to simulate LLM inference at a kernel level reduces the reliance on slow, deployment-specific benchmarking, accelerating the development and deployment cycles for generative AI applications.
- · AI developers
- · Cloud providers
- · Hardware manufacturers (GPUs)
- · LLM operators
- · Traditional benchmarking methods
- · Companies with suboptimal inference stacks
More efficient and cost-effective deployment of large language models becomes possible.
Accelerated innovation in LLM architectures and hardware optimization due to faster feedback loops.
Lower compute costs for AI inference could democratize access to advanced AI capabilities and expand their applications significantly.
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Read at arXiv cs.AI