Enabling Performant and Flexible Model-Internal Observability for LLM Inference
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909034113662976 |
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| author | Yu, Nengneng Xiong, Sixian Zhao, Yibo Wang, Wei Liu, Zaoxing |
| author_facet | Yu, Nengneng Xiong, Sixian Zhao, Yibo Wang, Wei Liu, Zaoxing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_11093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enabling Performant and Flexible Model-Internal Observability for LLM Inference Yu, Nengneng Xiong, Sixian Zhao, Yibo Wang, Wei Liu, Zaoxing Machine Learning Artificial Intelligence Performance Software Engineering Systems and Control 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. |
| title | Enabling Performant and Flexible Model-Internal Observability for LLM Inference |
| topic | Machine Learning Artificial Intelligence Performance Software Engineering Systems and Control |
| url | https://arxiv.org/abs/2605.11093 |