← 研踪 PaperTrailarXiv:2610.36564 · v1
DeepInfer-Mac: Running a 750B-Class MoE on a 24 GB Laptop
首次提交 2026-10-27 11:14
版本更新 2026-10-27 11:14
雷达观测 2026-10-29 00:35
摘要
DeepInfer-Mac runs the 750B-class DeepSeek-V4.1-Flash model on a MacBook Air with 24 GB of unified memory. A mixed low-bit conversion occupies 338 GiB on the internal SSD. About 7.6 GiB of weights remain resident; routed experts and Engram rows are loaded when needed. This separation makes the model executable, but it leaves each generated token waiting for a succession of storage reads. We organize expert weights so that reads fill reusable GPU buffers directly, and couple cache allocation with the lifetime of those buffers. In end-to-end runs, the system generates English, Chinese and code at 0.21-0.72 tokens per second, with a largest sampled process footprint of 16.73 GiB across the tested requests. Matched decode comparisons show a 21.0% throughput gain from next-layer prefetching over resident-expert overlap alone, at the cost of additional reads. Longer prompts remain costly. We describe the design, provide the implementation and measurement scripts, and identify the limits of this operating point: a model far larger than memory can run locally, while storage and prefill still govern the user's wait.
作者备注：17 pages, 4 figures
观测到的版本
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v1 · 2026-10-27 11:14 · 最新观测版本
DeepInfer-Mac: Running a 750B-Class MoE on a 24 GB Laptop
DeepInfer-Mac runs the 750B-class DeepSeek-V4.1-Flash model on a MacBook Air with 24 GB of unified memory. A mixed low-bit conversion occupies 338 GiB on the internal SSD. About 7.6 GiB of weights remain resident; routed experts and Engram rows are loaded when needed. This separation makes the model executable, but it leaves each generated token waiting for a succession of storage reads. We organize expert weights so that reads fill reusable GPU buffers directly, and couple cache allocation with the lifetime of those buffers. In end-to-end runs, the system generates English, Chinese and code at 0.21-0.72 tokens per second, with a largest sampled process footprint of 16.73 GiB across the tested requests. Matched decode comparisons show a 21.0% throughput gain from next-layer prefetching over resident-expert overlap alone, at the cost of additional reads. Longer prompts remain costly. We describe the design, provide the implementation and measurement scripts, and identify the limits of this operating point: a model far larger than memory can run locally, while storage and prefill still govern the user's wait.
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