Keeping AI memory on your own computer
Every product in this category is somebody else's server. There is another way, and a real trade-off attached to it.
The short answer: local AI memory means the files live on your disk in a format you can read without the tool that wrote them. You gain ownership, privacy and independence from any vendor. You lose access when the machine is off — which is the trade-off worth thinking about carefully rather than dismissing.
What local actually means
The word gets used loosely, so it is worth being precise. Local memory means three things together:
- The files are yours. Plain text or Markdown, in a folder you chose, readable in any editor.
- The index is derived. Whatever database powers search can be rebuilt from those files. If it is the only copy, you do not have local memory, you have a local database.
- The processing runs on your hardware. Embeddings and summarisation happen on your machine, not by sending your notes to an API.
Products that store your notes in a proprietary format on your disk meet the letter of this and not the spirit. The test is whether you can read your memory after uninstalling the software.
What it gets you
Nothing leaves. Not to a vendor, not to an embeddings API, not to a training set. For notes that mention clients, contracts or anything under an NDA, this is not a preference — it is the only version that passes review.
No lock-in that needs escaping. There is no export to request because there is nothing to unlock. That also means a company failing, pivoting or being acquired is not your problem.
No per-month cost for the part that runs on hardware you already own.
It keeps working. Offline, on a plane, when the vendor has an incident.
What it costs
The honest list, because the local-first case is usually made without one.
Your machine has to be on. This is the big one. Ask your assistant something from your phone at midnight and there is nothing to answer — not because the feature is missing, but because the memory is asleep in another room.
You need hardware that can run a model. A small model needs real VRAM; you can check what yours will handle with the VRAM calculator. CPU fallback works and is slower.
One machine, unless you solve sync. Local memory on a desktop is invisible to your laptop.
Backups are yours. Which is the point, and also a responsibility.
The trade-off nobody states plainly
You cannot have fully end-to-end encrypted memory that a server can also search semantically. Search requires computing over the content; if the server cannot read it, it cannot embed a new note or run similarity over the corpus. Encrypted-search research exists and is not production-ready for this.
So the real choice is: local only, and it works when your machine is on. Or a cloud replica, and the operator can technically read it. Anyone selling you both at once is describing something that does not exist yet.
Which means the question to ask any hosted memory product is not “is it encrypted?” — it is “who holds the keys?” If they answer with encryption-at-rest and change the subject, the answer is them.
How to build it yourself
The stack is not exotic: a folder of Markdown, a local model for embeddings, a small index for hybrid search, and a way for your assistant to query it. The Model Context Protocol is the current answer to that last part — a protocol-speaking server exposes your memory to Claude, ChatGPT, Gemini or Cursor without writing an integration per client.
The hard part is not the plumbing. It is the discipline about what gets stored, covered in what belongs in AI memory.
Where Varven sits
Varven is local-first with an optional replica: Markdown on your own disk as the primary copy, and a cloud copy you can switch on if you want answers when the machine is off. The free tier is local-only and permanent, and we say plainly on the security page that we hold the keys to the replica — because the alternative is implying otherwise.
Common questions
Can I run AI memory entirely offline?
Yes, if the assistant reading it also runs locally. If you are using a hosted assistant, your questions go to that vendor regardless — local memory controls where your notes live, not where inference happens.
What hardware do I need?
For a 7B-class model at common quantisation, roughly 6–8 GB of VRAM including context. An 8 GB card handles it; below that, CPU fallback works but is noticeably slower.
Is local memory more private than a cloud service with encryption?
Yes, and the difference is categorical rather than incremental. Encryption at rest protects against a stolen disk. It does not stop the operator reading your data, because they hold the keys. Local memory means there is no operator.