[2603.26469] UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models

Computer Science > Distributed, Parallel, and Cluster Computing arXiv:2603.26469 (cs) [Submitted on 27 Mar 2026] Title:UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models Authors:Doğaç Eldenk, Stephen Xia View a PDF of the paper titled UNIFERENCE: A Discrete Event Simulation Framework for Developing Distributed AI Models, by Do\u{g}a\c{c} Eldenk and 1 other authors View PDF HTML (experimental) Abstract:Developing and evaluating distributed inference algorithms remains difficult due to the lack of standardized tools for modeling heterogeneous devices and networks. Existing studies often rely on ad-hoc testbeds or proprietary infrastructure, making results hard to reproduce and limiting exploration of hypothetical hardware or network configurations. We present UNIFERENCE, a discrete-event simulation (DES) framework designed for developing, benchmarking, and deploying distributed AI models within a unified environment. UNIFERENCE models device and network behavior through lightweight logical processes that synchronize only on communication primitives, eliminating rollbacks while preserving the causal order. It integrates seamlessly with PyTorch Distributed, enabling the same codebase to transition from simulation to real deployment. Our evaluation demonstrates that UNIFERENCE profiles runtime with up to 98.6% accuracy compared to real physical deployments across diverse backends and hardware setups. By bridging simulation and deployment, UNI...