The proposed Wireless GPU Computing Infrastructure (WiCi) can reduce time to first token by up to 90%, improve the token rate by approximately 39x compared to local inference on mobile devices for the same model, and support much larger models.
Abstract
LLM inference applications are gaining significant traction. The demand for inference is growing exponentially, and the GPU usage of inference is increasingly surpassing that of training. Due to the mobility penalty, edge-side inference fails to deliver satisfactory performance. Consequently, most inference service providers currently rely on cloud-based inference, which incurs substantial, not sustainable costs for enterprises, and is even increasing in the agentic paradigm. Therefore, our goal is to enable powerful computing capabilities as server-grade GPUs on mobile devices. We propose Wireless GPU Computing Infrastructure (WiCi) in this paper. Through WiCi, mobile devices can wirelessly access server-grade GPUs, running inference tasks on mobile clients but offloading GPU-related computations to a nearby GPU via WiFi. WiCi introduces a series of designs to make sure the infrastructure is scalable with different applications, compatible with different mobile devices, and has comparable performance to running on a physical GPU. We test WiCi from mobile devices and find that WiCi can reduce time to first token by up to 90%, improve the token rate by approximately 39x compared to local inference on mobile devices for the same model, and support much larger models. WiCi also achieves up to nearly 80% of the native performance of the server-grade GPU across different applications.
This report aims to provide a comprehensive overview of lightweight model inference technologies in edge computing environments, mainly targeting low performance devices, such as mobile phones, intelligent equipment in vehicle, VR/AR headsets and Internet of Things (IoT).
Hengrui Bi· Applied and Computational En...· 0 citations
ATSInfer combines static tensor placement with load-aware dynamic transfer and introduces asynchronous CPU-GPU coordination to efficiently schedule hardware storage, data movement, and computation across heterogeneous backends and can substantially improve the user experience of local LLM deployment on personal consume...
Yangyijian Liu, Hong-Yi Ye, Ming-Yang Li et al.· arXiv.org· 0 citations
This paper examines the way phones and IoT devices run deep neural network (DNN) inference with help from a nearby edge server to minimize total user energy and connects theory, algorithms and experiments into one unified framework for energy minimization tasks.
Sumalatha Pilli, U. S. B. K. Maha Lakshmi, Thalakola Syamsundararao et al.· ITEGAM- Journal of Engineeri...· 0 citations
A time-varying integer program to minimize the long-term total cost of the edge AI inference system, including the inference latency, the inference error rate, the query-dispatching communication cost, and the energy consumption, subject to resource and workload constraints is proposed.
Ming-Tao Ji, Hehan Zhao, Lei Jiao et al.· Science China Information Sc...· 0 citations
A reinforcement learning-based computation offloading strategy using an improved Deep Deterministic Policy Gradient (IDDPG) algorithm that outperforms greedy offloading strategies and demonstrates improved efficiency in dynamic MEC environments.
S. Alam, Sami ud Din, Imtiaz Ali Shah et al.· International Journal of Inn...· 0 citations
A scalable edge-to-cloud AI inference pipeline in which inference tasks are dynamically distributed across heterogeneous edge and cloud resources is examined, providing a basis for resilient real-time AI systems while highlighting unresolved challenges involving heterogeneous hardware, dynamic workloads, privacy-utilit...
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