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200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management

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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 separate performance tests, a 32K-input, 64-output probe reaches 19.11 tokens/s, and a repeated 32K+6K workload has a median peak process footprint of 16,374 MiB.

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Original publisherarXiv.org
Canonical URLhttps://arxiv.org/abs/2609.17475
Publication timeThu, 17 Sep 2026 17:23:23 +0000
Retrieval time2026-09-17T17:23:44.840Z
Last seen2026-09-17T17:23:44.840Z
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Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

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Computer Science > Artificial Intelligence arXiv:2609.17475 (cs) [Submitted on 15 Sep 2026] Title:JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management Authors:Yuhua Chen View a PDF of the paper titled JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management, by Yuhua Chen View PDF HTML (experimental) Abstract: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.

Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.

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