How to Calculate If an AI Model Will Actually Run on Your Phone

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A technical analysis explains that running AI language models on smartphones depends not on total device RAM but on the per-process memory ceiling enforced by the operating system. The true memory footprint of a model is the sum of weight bytes, key-value cache, activation memory, and runtime overhead, all of which must stay below that process limit. Weight size depends on quantisation format, and commonly cited figures like '4-bit' underestimate real cost — the actual storage is closer to 4.6 bits per weight once group metadata is included. The key-value cache grows linearly with context length and can add nearly 1 GB for an 8,192-token context on a small 3B-parameter model. Developers are advised to calculate these figures before downloading any model, using device API calls to determine the actual per-process memory ceiling.

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