[2608.16157] FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution

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arXiv:2608.16157 (cs)

[Submitted on 17 Aug 2026]

Title:FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution

Authors:Shuo Yang, Xiaoze Fan, Melissa Pan, Haocheng Xi, Zhe Wang, Shanlin Sun, Kurt Keutzer, Song Han, Matei Zaharia, Chenfeng Xu, Ion Stoica<br>View a PDF of the paper titled FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution, by Shuo Yang and 10 other authors

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Abstract:Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the full serving stack, including model layout and loading, expert residency, CPU--GPU execution, agentic state reuse, and runtime memory management, around two realities of local AI: agent workloads continuously change their execution pattern, and edge hardware exposes heterogeneous resources whose balance differs from machine to machine. Rather than committing to a fixed offloading strategy, FreeToken continuously maps computation and model state onto the resources actually available. FreeToken supports more than 20 MoE models and real coding and tool-using agents across hardware ranging from an 8GB laptop GPU to a single workstation GPU. More importantly, it changes what these machines can practically serve, from a 35B model on a laptop to a 284B model on a gaming desktop and the 753B GLM-5.2 on a single workstation GPU. FreeToken turns open weights into deployable local software, making the machines users already own a practical platform for frontier-scale intelligence. We release the system at this http URL.

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Distributed, Parallel, and Cluster Computing (cs.DC)

Cite as:<br>arXiv:2608.16157 [cs.DC]

(or<br>arXiv:2608.16157v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.2608.16157

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arXiv-issued DOI via DataCite

Submission history<br>From: Shuo Yang [view email]<br>[v1]<br>Mon, 17 Aug 2026 06:22:53 UTC (1,344 KB)

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