You’ve probably seen people online running AI “locally” — meaning the AI lives on their own machine instead of on a company’s servers. It sounds cool, and sometimes it genuinely is. But before you talk yourself into a pricey upgrade, here’s the honest version of when it’s worth it and when it isn’t.
First, a quick myth-buster: Google’s NotebookLM isn’t local. It runs on Google’s servers using your uploaded files. The actually-local tools are things like Ollama, LM Studio, Jan, and AnythingLLM, which run open models (Llama, Mistral, Qwen, Gemma) entirely on your own computer. That difference matters, because privacy is usually the whole reason to go local in the first place.
Why local AI is fine for easy stuff but weaker on hard stuff:
- Size. The big hosted AIs are enormous. What fits on a regular gaming GPU is a much smaller model. Smaller models do everyday things fine — summarizing, simple questions, quick drafts — but fall behind on hard reasoning, tricky coding, and math.
- Compression. To make a model fit on your machine, the files get squeezed down (“quantized”). Great for light tasks, noticeably worse on anything that needs precision.
- No extras built in. Hosted assistants come wired up with web search, code running, and big memory. Locally, you have to build all that on yourself.
When running local actually makes sense:
- Privacy. This is the real one. If you’ve got stuff you genuinely shouldn’t send to a company — personal journals, sensitive documents — a local model that stays on your machine is worth it even if it’s not the smartest.
- No internet. Off-grid, spotty connection, or a locked-down network.
- Doing the same simple task a ton. Tagging, sorting, or drafting the same kind of thing thousands of times without paying per use.
- Asking questions about your own files. This leans more on search-through-your-docs than raw brainpower, so a modest model does okay.
- Learning and tinkering. Honestly a great reason on its own if you’re curious how this stuff works.
The bottom line: Don’t buy expensive hardware just to get a smarter AI — you won’t beat the hosted ones that way, and it’ll cost a fortune. Buy hardware only if privacy, offline use, or high volume is your real reason. For everyday stuff, whatever computer you already have is fine, and you can reach for a free hosted AI when the task gets hard. You’re not missing out by skipping the $2,000 rig.
Sources:
- Ollama, LM Studio, Jan, and AnythingLLM — the official sites for the main tools that run open models on your own machine.
- NVIDIA, “How to Get Started With Large Language Models on RTX PCs” — explains running LLMs locally for privacy and control, and the quality trade-offs involved.
- MindStudio, “How to Run Open-Weight AI Models Locally” — what quantization is and why model size drives VRAM needs (a 70B model at full precision needs roughly 140GB).
- Google Cloud, NotebookLM documentation — confirms NotebookLM runs in Google’s cloud, not on your device.