你的电脑能跑753B大模型了
大模型通常被认为只能在数据中心跑，但 FreeToken 系统让个人电脑也能运行超大模型：一台 8GB 显存的笔记本能跑 35B 模型，游戏台式机能跑 284B，工作站显卡甚至能跑 753B 的 GLM-5.2。它不靠固定策略，而是实时把计算和模型数据分配到 CPU、GPU 等所有可用资源上，并针对 AI 代理工作负载的动态变化做了优化。这不是你明天就能装上的软件，但它意味着开源大模型正在从云端走向你的本地设备，隐私和离线使用的前景值得关注。
📄 原文摘要(英文)
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 flashml.ai.