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An intro about oneinfer-edge

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We just shipped the first feature for oneinfer-edge and it's open source.

Ever copy a Hugging Face model ID, spend 2 hours setting things up, and then watch it fail because your VRAM was off by a few GB? Yeah. We've all been there.

oneinfer-edge now tells you if your machine can run any Hugging Face model before you deploy.

Paste a model ID, it scans your GPU, VRAM, OS, and serving libraries, gives you a Hardware Ready verdict and full memory breakdown (weights + KV cache + serving overhead).

No surprises at runtime.

Supports Apple Silicon (M1 to M5), NVIDIA (CUDA), AMD (ROCm), and serving libraries including Ollama, llama.cpp, SGLang, TensorRT-LLM, PyTorch and many more coming.

It tells you why something won't work, not just that it won't.

CPU support is something we're actively working through and feedback and contributions on that front are very welcome.

oneinfer-edge is part of the broader inference control plane, a platform built for teams shipping multimodal AI products at scale.

oneinfer-edge brings that same infrastructure intelligence to your local machine so self-hosting is a genuine alternative to managed cloud inference, not a debugging exercise.

We built this in the open because self-hosted AI infrastructure should belong to the community that runs it.

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Drop us a star if this looks useful and PRs are wide open. We're just getting started.

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Hey Product Hunt! 👋 I'm Dani, the developer of NEXIS-X. I built this because I was tired of every AI tool sending my data to some server I have no control over. So I made one that runs entirely on your own machine — no cloud, no tracking, no subscription. What makes NEXIS-X different: 🧠 Real agent loop — the AI decides when to use tools, not hardcoded rules 💾 Two-level memory system — remembers across sessions 🔒 PIN protection & fully offline by default 🌐 Optional web search when you need it 🆓 Completely free It works with any GGUF model from HuggingFace, so you're not locked into anything either. Would love to hear your feedback — especially from developers who want to dig into the agent architecture. Ask me anything! 🚀(the product is here: )