This research introduces a distributed on-device LLM inference framework that splits large language model processing across multiple edge devices, making powerful AI accessible on resource-constrained hardware.

• Leverages tensor parallelism to partition neural network weights across multiple devices
• Implements a communication-efficient approach optimized for wireless networks
• Reduces computational burden on individual devices while maintaining inference capabilities
• Enables edge AI deployment without requiring cloud connectivity

This engineering breakthrough addresses a critical bottleneck in edge AI deployment, allowing sophisticated language models to run on everyday devices without constant cloud connectivity, potentially transforming mobile and IoT applications.

Communication-Efficient Distributed On-Device LLM Inference Over Wireless Networks