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JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management | AI Learning Platform

MultimodalGPU NetworkingReasoningInference OptimizationServing InfrastructureQuantizationKV CacheCost Optimization

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

Yuhua Chen1 min readarXivSep 15, 2026

Key Takeaway for Students

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

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This paper presents a significant advancement in local LLM serving, enabling much larger context windows on consumer-grade hardware. For ML engineers, this means more powerful local development environments and potentially new application paradigms for edge AI, reducing reliance on cloud infrastructure for certain tasks.

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Career Insight

high demand

Why it matters

Skills to learn

Understanding KV cache management strategies (e.g., KVExec)Familiarity with MLX framework for Apple SiliconTechniques for memory-efficient LLM inference (e.g., PhaseSwap, StateTrans)Profiling and optimizing LLM memory usage on constrained devicesImplementing just-in-time materialization for model components

Interview questions

Explain the challenges of serving large context LLMs on devices with limited memory, and how JustFit addresses these challenges through its core mechanisms.
How does 'just-in-time materialization' differ from traditional memory management for LLMs, and what are its implications for latency and throughput?
Design a system for serving a 70B parameter LLM on a device with 32GB of RAM, optimizing for maximum context window and minimizing inference latency. How would you incorporate principles from JustFit?

Relevant for ~20 months · generated by the enrichment pipeline