Transferable Latency Prediction for Fast LLM Screening on Heterogeneous Edge Devices
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arXiv:2607.21602v1 Announce Type: new Abstract: Accurate latency prediction is critical for deploying large language models (LLMs) on heterogeneous edge devices, where inference latency is affected by model architecture, prompt behavior, runtime backend, hardware utilization, dynamic voltage and frequency scaling (DVFS), and thermal variation. This paper presents a runtime-aware latency prediction framework for deployment-oriented LLM selection. The framework represents each inference request…
1Key Takeaways
- This paper presents a runtime-aware latency prediction framework for deployment-oriented LLM selection.
- The framework represents each inference request….
2AIWedia Score
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3Why it matters
Research breakthroughs often arrive in products months later—early signals matter for strategy. arXiv cs.AI reports that this paper presents a runtime-aware latency prediction framework for deployment-oriented LLM selection.
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