CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration

• URL: http://arxiv.org/abs/2411.02829v1
• Date: Tue, 05 Nov 2024 06:00:27 GMT
• Title: CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration
• Authors: Hongpeng Jin, Yanzhao Wu,
• Abstract summary: Large Language Models (LLMs) have achieved remarkable success in serving end-users with human-like intelligence. LLMs demand high computational resources, making it challenging to deploy them to satisfy various performance objectives. We introduce CE-CoLLM, a novel cloud-edge collaboration framework that supports efficient and adaptive LLM inference for end-users at the edge.
• Score: 1.6021932740447968
• License:
• Abstract: Large Language Models (LLMs) have achieved remarkable success in serving end-users with human-like intelligence. However, LLMs demand high computational resources, making it challenging to deploy them to satisfy various performance objectives, such as meeting the resource constraints on edge devices close to end-users or achieving high accuracy with ample resources. In this paper, we introduce CE-CoLLM, a novel cloud-edge collaboration framework that supports efficient and adaptive LLM inference for end-users at the edge with two modes, (1) low-latency edge standalone inference and (2) highly accurate cloud-edge collaborative inference. First, we show that the inherent high communication costs for transmitting LLM contextual information between the edge and cloud dominate the overall latency, making it inefficient and costly to deploy LLMs using cloud-edge collaboration. Second, we propose several critical techniques to address this challenge, including early-exit mechanism, cloud context manager, and quantization in cloud-edge collaboration to enable not only low-latency standalone edge inference but also efficient and adaptive cloud-edge collaborative inference for LLMs. Third, we perform comprehensive experimental analysis, which demonstrates that CE-CoLLM significantly reduces inference time by up to 13.81% and cloud computation costs by up to 84.55% compared to the popular cloud-based LLM deployment, while maintaining comparable model accuracy. The proposed approach effectively shifts the computational load to the edge, reduces the communication overhead, scales efficiently with multiple edge clients, and provides reliable LLM deployment using cloud-edge collaboration.

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