Elastic On-Device LLM Service
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arXiv
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| Format: | Preprint |
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2024
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| author | Yin, Wangsong Yi, Rongjie Xu, Daliang Huang, Gang Xu, Mengwei Liu, Xuanzhe |
| author_facet | Yin, Wangsong Yi, Rongjie Xu, Daliang Huang, Gang Xu, Mengwei Liu, Xuanzhe |
| contents | On-device Large Language Models (LLMs) are transforming mobile AI, catalyzing applications like UI automation without privacy concerns. Nowadays the common practice is to deploy a single yet powerful LLM as a general task solver for multiple requests. We identify a key system challenge in this paradigm: current LLMs lack the elasticity to serve requests that have diversified Service-Level Objectives (SLOs) on inference latency. To tackle this, we present \sys, an on-device LLM service that elasticizes both the model and the prompt dimension of a full LLM. It incorporates (1) a one-shot neuron-reordering method, which leverages the intrinsic permutation consistency in transformer models to generate high-quality elasticized sub-models with minimal runtime switching overhead; (2) a dual-head tiny language model, which efficiently and effectively refines the prompt and orchestrates the elastification between model and prompt. We implement such an elastic on-device LLM service on multiple COTS smartphones, and evaluate \sys on both standalone NLP/mobile-agent datasets and end-to-end synthesized traces. On diverse SLOs, \sys outperforms 7 strong baselines in (absolute) accuracy by up to 14.83\% and 10.45\% on average, with <1\% TTFT switching overhead, on-par memory consumption and <100 offline GPU hours. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09071 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Elastic On-Device LLM Service Yin, Wangsong Yi, Rongjie Xu, Daliang Huang, Gang Xu, Mengwei Liu, Xuanzhe Distributed, Parallel, and Cluster Computing Artificial Intelligence On-device Large Language Models (LLMs) are transforming mobile AI, catalyzing applications like UI automation without privacy concerns. Nowadays the common practice is to deploy a single yet powerful LLM as a general task solver for multiple requests. We identify a key system challenge in this paradigm: current LLMs lack the elasticity to serve requests that have diversified Service-Level Objectives (SLOs) on inference latency. To tackle this, we present \sys, an on-device LLM service that elasticizes both the model and the prompt dimension of a full LLM. It incorporates (1) a one-shot neuron-reordering method, which leverages the intrinsic permutation consistency in transformer models to generate high-quality elasticized sub-models with minimal runtime switching overhead; (2) a dual-head tiny language model, which efficiently and effectively refines the prompt and orchestrates the elastification between model and prompt. We implement such an elastic on-device LLM service on multiple COTS smartphones, and evaluate \sys on both standalone NLP/mobile-agent datasets and end-to-end synthesized traces. On diverse SLOs, \sys outperforms 7 strong baselines in (absolute) accuracy by up to 14.83\% and 10.45\% on average, with <1\% TTFT switching overhead, on-par memory consumption and <100 offline GPU hours. |
| title | Elastic On-Device LLM Service |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2409.09071 |