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Explains how to run AI models directly on Linux without the cloud. Covers why local inference matters (privacy, latency, cost), necessary CPU/GPU hardware and drivers, installing runtimes, managing models and dependencies, using containers or virtual envs, quantization to shrink footprint, and practical tuning tips for performance, storage, and fully offline workflows.