The Privacy Tax: Why Local LLMs Are the Only Real Way to Own Your Data

As Wired first reported, if you are using ChatGPT, Claude, or Perplexity, you are not the owner of your AI experience; you are a tenant. In my view, the only way to achieve true data privacy is to move the model off the cloud and onto your own silicon.

Running an LLM locally means you are no longer sending information to the cloud for external analysis, granting you offline access and a level of privacy impossible with centralized providers. However, this sovereignty comes with a cost. According to Wired, you need significant RAM—at least 8 GB, though 16 GB is better, and 32 GB or more is required for the fastest, largest models.

Hardware choice matters: a dedicated Nvidia GPU is a massive advantage for Windows users, while Apple Silicon is preferred for Mac users due to its unified CPU, GPU, and RAM.

Beyond hardware, there is the "tedium tax." You lose the seamless convenience of a polished app and must handle all updates yourself. Setting up the environment requires both a model—available via repositories like Hugging Face—and software to run it. Wired suggests LM Studio Bionic as a user-friendly, free option, though more technical tools like Ollama, Llama.cpp, vLLM, and GPT4All also exist.

Local models generally aren't as speedy or advanced as paid cloud versions, but in a world where every keystroke is potential training data, owning the hardware is the only way to own your data.