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

arXiv: https://arxiv.org/abs/2503.14882 · HTML: https://mlsanity.com/paper/2503.14882/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2025.

## Built on by (most cited first, 9 of 9)

- [Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques](https://mlsanity.com/paper/2506.06579/) (2025)
- [OrderMoE: An expert similarity driven distributed edge MoE inference](https://mlsanity.com/paper/2607.17154/) (2026)
- [Federated Attention: A Distributed Paradigm for Collaborative LLM Inference over Edge Networks](https://mlsanity.com/paper/2511.02647/) (2025)
- [Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks](https://mlsanity.com/paper/2512.06784/) (2025)
- [Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices](https://mlsanity.com/paper/2605.15694/) (2026)
- [Multi-SPIN: Multi-Access Speculative Inference for Cooperative Token Generation at the Edge](https://mlsanity.com/paper/2606.04581/) (2026)
- [TrimMoE A communication aware and adaptive depth framework for distributed edge inference](https://mlsanity.com/paper/2608.00573/) (2026)
- [BALANCE: Hybrid Autoregressive-Speculative LLM Inference at the Network Edge](https://mlsanity.com/paper/2608.05926/) (2026)
- [From Semantic to Token Communication: The Next Paradigm for Large-Model-Driven 6G Intelligent Connectivity](https://mlsanity.com/paper/2609.10714/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have demonstrated remarkable success across various application domains, but their enormous sizes and computational demands pose significant challenges for deployment on resource-constrained edge devices. To address this issue, we propose a novel distributed on-device LLM inference framework that leverages tensor parallelism to partition the neural network tensors (e.g., weight matrices) of one LLM across multiple edge devices for collaborative inference. A key challenge in tensor parallelism is the frequent all-reduce operations for aggregating intermediate layer outputs across participating devices, which incurs significant communication overhead. To alleviate this bottleneck, we propose an over-the-air computation (AirComp) approach that harnesses the analog superposition property of wireless multiple-access channels to perform fast all-reduce steps. To utilize the heterogeneous computational capabilities of edge devices and mitigate communication distortions, we investigate a joint model assignment and transceiver optimization problem to minimize the average transmission error. The resulting mixed-timescale stochastic non-convex optimization problem is intractable, and we propose an efficient two-stage algorithm to solve it. Moreover, we prove that the proposed algorithm converges almost surely to a stationary point of the original problem. Comprehensive simulation results will show that the proposed framework outperforms existing benchmark schemes, achieving up to 5x inference speed acceleration and improving inference accuracy.