ELMS: Elasticized Large Language Models On Mobile Devices

• URL: http://arxiv.org/abs/2409.09071v1
• Date: Sun, 8 Sep 2024 06:32:08 GMT
• Title: ELMS: Elasticized Large Language Models On Mobile Devices
• Authors: Wangsong Yin, Rongjie Yi, Daliang Xu, Gang Huang, Mengwei Xu, Xuanzhe Liu,
• Abstract summary: On-device Large Language Models (LLMs) are revolutionizing mobile AI, enabling applications such as UI automation while addressing privacy concerns. We introduce ELMS, an on-device LLM service designed to provide elasticity in both the model and prompt dimensions. A one-time reorder neuroning technique, which utilizes the inherent permutation consistency within transformer models to create high-quality, elastic sub-models. A dual-head compact language model, which efficiently refines prompts and coordinates the elastic adaptation between the model prompt.
• Score: 5.689405542579458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On-device Large Language Models (LLMs) are revolutionizing mobile AI, enabling applications such as UI automation while addressing privacy concerns. Currently, the standard approach involves deploying a single, robust LLM as a universal solution for various applications, often referred to as LLM-as-a-Service (LLMaaS). However, this approach faces a significant system challenge: existing LLMs lack the flexibility to accommodate the diverse Service-Level Objectives (SLOs) regarding inference latency across different applications. To address this issue, we introduce ELMS, an on-device LLM service designed to provide elasticity in both the model and prompt dimensions of an LLMaaS. This system includes: A one-time neuron reordering technique, which utilizes the inherent permutation consistency within transformer models to create high-quality, elastic sub-models with minimal runtime switching costs. A dual-head compact language model, which efficiently refines prompts and coordinates the elastic adaptation between the model and the prompt. We have implemented this elastic on-device LLM service on several off-the-shelf (COTS) smartphones and evaluate ELMS using both standalone NLP/mobile-agent datasets and synthesized end-to-end traces. Across a range of SLOs, ELMS surpasses four strong baselines by up to 16.83% and 11.04% in absolute accuracy on average, with less than 1% Time-To-First-Token (TTFT) switching overhead, comparable memory usage, and fewer than 100 offline GPU hours.

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