JustFit: 200K-Token LLM Serving on a 24 GiB Laptop with Just-in-Time State Management
Yuhua Chen
arXiv:2609.17475v1Today’s standout is JustFit, a systems paper that tackles a very practical bottleneck: how to run very large language models on a normal laptop without running out of memory. The challenge is not just model weights, but the huge amount of context and execution state that accumulates during long prompts and long generations. JustFit introduces a runtime that manages this state just in time, using compressed key-value execution, swapping components in and out of memory, and preserving state across serving transitions. The result is striking: on a 24-gigabyte MacBook, it can handle around 200,000 input tokens plus 16,000 output tokens, far beyond typical local inference limits. Why this matters is simple: it moves advanced LLM use from servers into everyday devices, making private, low-latency, offline coding and reasoning much more feasible. It’s a strong example of systems work unlocking capabilities that model scaling alone can’t deliver.
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