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arXiv · 2607.12875

Automatic Model-Hardware Co-Adaptation for Heterogeneous AI Accelerators

Tian Chen·Mingheng Mi·Pu Wang·Chen Hu·Hai Zhang
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Abstract

Large language models now evolve faster than production inference systems can be ported and optimized. New releases change attention, MoE routing, quantization formats, KV-cache layout, and parallel execution patterns, while deployed accelerator fleets remain heterogeneous across hardware generations, framework forks, operator libraries, compiler backends, and communication runtimes. Serving a new model on existing hardware is therefore a model-framework-kernel-hardware co-adaptation problem. We present MetaInfer, an LLM-agent system that formulates inference adaptation as route search over a costed execution-adaptation graph. The graph connects model semantics, framework dispatch, kernel choices, hardware capabilities, runtime evidence, and serving objectives. MetaInfer constructs and updates this graph during execution, restores missing or blocked routes through patches, and reduces route cost through staged validation and end-to-end profiling. Three real episodes -- DeepSeek V4 Flash on NVIDIA A800, GLM 5.3 Flash on NVIDIA A800, and DeepSeek V4 Flash on Hygon K100AI DCU -- demonstrate deployment repair, cross-model knowledge transfer, and portability across heterogeneous accelerator software stacks.

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Tian Chen, Mingheng Mi, Pu Wang, Chen Hu, Hai Zhang. 2026-09-01. Automatic Model-Hardware Co-Adaptation for Heterogeneous AI Accelerators. https://arxiv.org/abs/2607.12875

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