LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices (2312.00388v1)

Abstract: Deploying LLMs locally on mobile devices presents a significant challenge due to their extensive memory requirements. In this paper, we introduce LinguaLinked, a system for decentralized, distributed LLM inference on mobile devices. LinguaLinked enables collaborative execution of the inference task across multiple trusted devices. LinguaLinked ensures data privacy by processing information locally. LinguaLinked uses three key strategies. First, an optimized model assignment technique segments LLMs and uses linear optimization to align segments with each device's capabilities. Second, an optimized data transmission mechanism ensures efficient and structured data flow between model segments while also maintaining the integrity of the original model structure. Finally, LinguaLinked incorporates a runtime load balancer that actively monitors and redistributes tasks among mobile devices to prevent bottlenecks, enhancing the system's overall efficiency and responsiveness. We demonstrate that LinguaLinked facilitates efficient LLM inference while maintaining consistent throughput and minimal latency through extensive testing across various mobile devices, from high-end to low-end Android devices. In our evaluations, compared to the baseline, LinguaLinked achieves an inference performance acceleration of $1.11\times$ to $1.61\times$ in single-threaded settings, $1.73\times$ to $2.65\times$ with multi-threading. Additionally, runtime load balancing yields an overall inference acceleration of $1.29\times$ to $1.32\times$.

Exploring LinguaLinked: Distributed LLM Inference on Mobile Devices

Introduction

The advent of LLMs has been a cornerstone in advancing NLP tasks, offering substantial improvements in text generation, machine translation, and summarization, among other applications. However, the considerable memory requirements of LLMs pose challenges for deployment, especially on resource-constrained devices like smartphones. In this context, the paper introduces LinguaLinked, a novel system designed for decentralized, distributed LLM inference on mobile devices. This system not only addresses the challenges of deploying LLMs on such devices but also ensures data privacy by processing information locally on trusted devices.

Key Strategies and System Design

LinguaLinked leverages three main strategies to achieve efficient distributed inference: optimized model assignment, an optimized data transmission mechanism, and runtime load balancer. These components work synergistically to enhance system responsiveness and throughput, demonstrating significant performance improvements across various mobile devices.

• Optimized Model Assignment: This technique involves segmenting LLMs and using linear optimization to align segments with each device's capabilities, thus minimizing memory and computational burden.
• Optimized Data Transmission: Ensures structured and efficient data flow between model segments while maintaining the integrity of the original model structure, optimizing latency in data transmissions.
• Runtime Load Balancer: Monitors and redistributes tasks among mobile devices to prevent bottlenecks, thereby enhancing the overall efficiency of the system.

Evaluation and Performance

LinguaLinked was evaluated across high-end and low-end Android devices, demonstrating inference performance acceleration ranging from 1.11× to 1.61× in single-threaded settings, and 1.73× to 2.65× with multi-threading. Additionally, runtime load balancing yielded an overall inference acceleration of 1.29× to 1.32×. Furthermore, the system was shown to facilitate efficient inference for both full-precision and quantized LLMs, with notable improvements especially for larger models.

Theoretical and Practical Implications

The development of LinguaLinked marks a significant step towards deploying sophisticated LLMs directly onto mobile devices, expanding the horizons of NLP applications in mobile computing environments. The system's design offers a scalable solution that balances computational demands with device capabilities, ensuring efficient data privacy handling by localizing data processing. Moreover, LinguaLinked's strategies could set precedents for future research into distributed computing models and systems in the context of AI deployment, particularly in addressing challenges associated with resource-constrained environments.

Future Directions

Though LinguaLinked demonstrates promising advancements, it also opens avenues for further research. Potential directions include exploring adaptive algorithms to further optimize resource allocation and computational load balancing, considering thermal management and energy efficiency, and expanding support for diverse model types beyond LLMs. As hardware and software frameworks continue to evolve, so too will the capabilities and applications of systems like LinguaLinked, driving the efficient and localized deployment of cutting-edge AI technologies.

Conclusion

LinguaLinked presents a pioneering approach to the decentralized, distributed inference of LLMs on mobile devices, addressing the computational and memory limitations inherent in such environments. By optimizing model assignment, data transmission, and runtime load balancing, LinguaLinked significantly enhances the performance of LLM inference tasks, paving the way for broader, more efficient deployment of AI applications in mobile settings. As we look towards the future, the principles and methodologies underpinning LinguaLinked will undoubtedly influence ongoing efforts to bridge the gap between advanced AI models and mobile computing capabilities.