<ul data-eligibleForWebStory="false"><li>Large Language Models (LLMs) are versatile and can be fine-tuned with parameter-efficient adapters like Low-Rank Adaptation (LoRA) for efficient adaptation to downstream tasks.</li><li>Deploying fine-tuned LLMs on multi-tenant edge devices can reduce latency, enhance privacy, and provide personalized responses but poses challenges in efficient serving due to adapter complexity and memory overhead.</li><li>A new system called EdgeLoRA addresses these challenges by introducing adaptive adapter selection, heterogeneous memory management, and batch LoRA inference, resulting in significant improvements in latency and throughput over existing methods.</li><li>EdgeLoRA shows up to a 4 times increase in throughput and the ability to handle multiple adapters simultaneously, indicating its potential to enhance LLM edge deployment in multi-tenant environments.</li></ul>

EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices

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