ai.hackcv
论文精选 65arXiv

JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management· JustFit:24GB 笔记本上的 20 万_token 大模型服务

Capable open-weight models make local coding and reasoning attractive, but their context and execution state strain laptop memory. We present JustFit, an MLX-based inference runtime that combines KVExec for compressed KV execution, PhaseSwap for component residency, and StateTrans for state-preserving serving transitions. These mechanisms fuse reconstruction and coordinate just-in-time materialization and release, independently of model-weight quantization. In full-execution capacity tests on a 24 GiB M4 Pro MacBook running Qwen3.8-27B MXFP4, three independent runs complete 196,608 input and 16,384 output tokens, increasing completed single-request context from the mlx-vlm baseline's 30,720 positions to 212,992 (6.93x); a separate two-request run retains 229,376 positions in aggregate. In

AI 解读论文

24GB 笔记本上实现 20 万 token 的大模型服务

核心方法
提出 JustFit,结合 KVExec、PhaseSwap 和 StateTrans 机制,实现压缩执行、组件驻留和状态保持的动态管理
适合谁读
研究者 / 工程师
要解决的问题
大模型在本地运行时,上下文和执行状态占用大量内存,限制了其应用范围
关键实验
在 24 GiB 内存的 MacBook 上运行 Qwen3.8-27B MXFP4 模型,单请求上下文容量从 30,720 扩展到 212,992,两请求总容量达 229,376
主要贡献
显著提高了大模型在有限内存设备上的执行效率和上下文容量
意义与局限
使得大模型在便携设备上的应用成为可能,但可能需要进一步优化以适配更多设备和模型
领域:cs.AI作者:Yuhua Chen
相关推荐
论文精选 65已解读

Simulated Control Evals· 模拟控制评估

Control evaluations measure whether safeguards can prevent an AI model from causing undesi…

领域:cs.AI作者:Jeffrey Olmo、David Zhang、Tyler Tracy
📎 arXiv🕒 10-23 01:26🔗 arxiv.org

本站内容由 LLM 精选聚合,原文版权归 arXiv 所有 · 摘录仅供参考