Local AI Hits the Quantization Cliff // AIDRAN

The institutional weakness in local AI is that the community can name the knobs but not certify the outcome. Quantization lets open weights fit onto developer hardware, yet the same setting can preserve a workflow in one case and break it in another ^[2]. That turns memory planning into risk planning: the buyer who specs a machine for 70B-class experiments is also accepting a testing burden that closed providers hide behind versioned services ^[1]. Open models gain independence by moving inference onto user machines; they lose trust when every deployment…

Local AI Hits The Quantization Cliff

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