FreeToken debuts edge-native MoE serving, running 753B model on a single workstation GPU

[HF Papers] A preprint titled FreeToken: Efficient Edge-Native MoE Serving with Bandwidth-Adaptive Execution, by Shuo Yang, Xiaoze Fan, Melissa Pan, Haocheng Xi, Zhe Wang and Shanlin Sun, presents FreeToken, an edge-native MoE serving system that treats a personal machine as a unified, elastic inference platform rather than a small GPU. According to the paper, FreeToken co-designs the full serving stack, including model layout and loading, expert residency, CPU-GPU execution, agentic state reuse and runtime memory management, and continuously maps computation and model state onto actually available resources instead of committing to a fixed offloading strategy. The authors say it supports more than 20 MoE models and real coding and tool-using agents across hardware from an 8GB laptop GPU to a single workstation GPU, scaling from a 35B model on a laptop and a 284B model on a gaming desktop to the 753B GLM-5.2 on a single workstation GPU. The system is released at flashml.ai. These are unreviewed preprint findings.