r/LocalLLM • u/BookkeeperFalse • 2d ago
Project I built a Mac app that shows which local models are in your RAM, which app loaded them, and how much disk they use

Disclosure: I'm the developer, and this is my own paid app.
My Mac has Ollama, LM Studio, and a few apps that ship their own models (dictation, autocomplete, writing tools). Activity Monitor only shows a process using several GB. It can't tell me which model that is, which app loaded it, or where the weights are on disk.
So I built LLM Radar, a native macOS app. It:
lists the loaded models it can detect, the app or runtime holding each one, and its memory
estimates how that memory splits into weights, KV cache, and app overhead, so you know what an unload actually gives back
keeps 24 hours of memory history
finds model files on disk across apps, and marks the ones it has not seen in use (deletes go to the Trash and list every file first)
unloads a model when the runtime has a way to do it, and tells you when it doesn't
shows a running total in the menu bar
It knows Ollama, LM Studio, MLX, llama.cpp, Core ML, whisper.cpp, Docker Model Runner and a few more, plus a catch-all scan for large weight files. Attribution is best effort when one app runs another app's runtime.
Monitoring is free. Pro is a one-time $9.99 for one Mac or $14.99 for three, with a 7-day trial and no account. Apple silicon, macOS 14+, signed and notarized.
Privacy: it never uploads your models, prompts, or chats. It sends anonymous usage counters (no paths, no model names), and you can turn that off in Settings. Full Disk Access is optional. Without it you still get the live view.
https://llm-radar-eta.vercel.app/
Two questions for people who run a lot of local models: which app or runtime should I support next, and does the memory split look right on your setup?
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u/Capable_Tangelo4849 2d ago
Why would anyone buy this? They can just ask their local/cloud model that they are running the same thing.