Developer finds local AI dream stalled by hardware costs and model limitations

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A software developer spent several months attempting to run a fully offline AI assistant on a personal Mac with 24GB of RAM, only to find that capable language models demand more memory than consumer machines typically offer. The effort was further complicated by a sharp rise in hardware costs, with DRAM prices roughly doubling since early 2025 and some SSD prices surging even more steeply. The author attributes the shortage directly to large-scale AI datacenter buildouts, which are projected to consume around 70% of global high-end memory supply this year, with cloud giants locking up chip production years in advance. After testing several local AI interfaces — including Pi, OpenCode, and Hermes — the developer concluded that no software wrapper could compensate for insufficient memory. Ultimately, tasks requiring reliable judgment still pushed the author back to cloud-based AI services, leaving the goal of full local independence unmet.

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