Enabling Performant and Flexible Model-Internal Observability for LLM Inference

Nengneng Yu; Sixian Xiong; Yibo Zhao; Wei Wang; Zaoxing Liu

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arXiv · 2605.11093

Enabling Performant and Flexible Model-Internal Observability for LLM Inference

Nengneng Yu·Sixian Xiong·Yibo Zhao·Wei Wang·Zaoxing Liu

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Abstract

Today's inference-time workloads increasingly depend on timely access to a model's internal states. We present DMI-Lib, a high-speed deep model inspector that treats internal observability as a first-class systems primitive, decoupling it from the inference hot path via an asynchronous observability substrate built from Ring^2, a GPU-CPU memory abstraction for capturing and staging tensors, and a policy-controlled host backend that exports them. DMI-Lib enables the placement of observation points across a rich space of internal signals and diverse inference backends while preserving serving optimizations and adhering to tight GPU memory budgets. Our experiments demonstrate that DMI-Lib incurs only 0.4%--6.8% overhead in offline batch inference and an average of 6% in moderate online serving, reducing latency overhead by 2x-15x compared to existing baselines with similar observability features. DMI-Lib is open-sourced at https://github.com/ProjectDMX/DMI.

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BibTeX RIS

Nengneng Yu, Sixian Xiong, Yibo Zhao, Wei Wang, Zaoxing Liu. 2026-05-11. Enabling Performant and Flexible Model-Internal Observability for LLM Inference. https://arxiv.org/abs/2605.11093

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