QuantaMind

Can your local model actually run an agent? Test it - OSS.

7 followers

Can your local model actually run an agent? Test it - OSS.

7 followers

Open-source desktop App that tells you whether a local model can actually run your coding agent, before you waste days finding out it can't. It runs the real agent loop on your own hardware, fully offline, and returns a Ready - Not Ready verdict per model and quant. Public benchmarks don't test your GPU, your VRAM, your quantization, a model that scores well can still hallucinate "done" at step 7. Open source. Works with Ollama, llama.cpp, and MLX.

Free

Launch tags:Open Source•Developer Tools•Artificial Intelligence

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Framer AI AgentsDesign and publish professional sites with AI

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Maker

📌

Hey PH 👋 I built QuantaMind because I kept getting burned by local models that looked great on paper and fell apart the moment I put them in an agent loop. The pattern was always the same: a model scores well on some public leaderboard, I load it locally, and then it quietly breaks, a malformed tool call at step 7, or worse, it says "task complete" when nothing got done. Public benchmarks test the model in the cloud. They don't test your GPU, your VRAM, or your quantization, and that combination is exactly what breaks. The question I heard everywhere (and asked myself constantly) was: "I have X GB of unified memory / vRAM, which model actually works?" The honest answer today is a month of trial and error. QuantaMind runs the real agent loop on your own hardware, fully offline, and gives you a Ready / Conditional / Not Ready verdict per model and quant — with the reasons named, never a bare score. It works with Ollama, llama.cpp, and MLX. A few things I care about that shaped it: - It verifies the actual tool calls happened, not the model's claim of "done" — a trivial do-nothing agent scores ~0, not the inflated numbers you see on some benchmarks. - It never fabricates a metric. If something can't be measured on your backend, it says N/A. - It's fully open source and runs entirely on your machine. no prompts or data ever leave. It's early and I'd genuinely love feedback from people running local models day to day. If you've hit the "fits in memory but still fails" wall, I'd love to hear what model/hardware combo burned you, and contributions are very welcome. One thing I'm curious about: what's the smallest quant you've actually trusted in a real agentic workflow?

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2mo ago

Finally something that stops me from waiting a day only to watch an agent loop crash on step 6. Ran it on a few quant variants and it nailed my VRAM limits every time, way more useful than the abstract leaderboards I've been guessing from.

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2mo ago

Maker

@nuriyepbrr Thanks. "crash on step 6" is exactly the pain I built it for, and glad the per-quant VRAM read landed; that quant × hardware combo is the thing leaderboards can't show you.

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2mo ago

Thanks for the tool! Any plans on adding LM Studio?

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19d ago

Reviews

Maker

📌

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2mo ago

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2mo ago

Maker

@nuriyepbrr Thanks. "crash on step 6" is exactly the pain I built it for, and glad the per-quant VRAM read landed; that quant × hardware combo is the thing leaderboards can't show you.

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2mo ago

Thanks for the tool! Any plans on adding LM Studio?

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19d ago