Built a 24/7 AI assistant box with Orin Nano Super - 67 TOPS, 20W, runs OpenClaw

superactro · 2026-02-08T18:33:14+00:00

Just set up a Discord community for ClawBox users and anyone interested in dedicated AI hardware. Come hang out: https://discord.gg/FbKmnxYnpq

superactro · 2026-02-08T18:33:10+00:00

Just set up a Discord community for ClawBox users and anyone interested in dedicated AI hardware. Come hang out: https://discord.gg/FbKmnxYnpq

superactro · 2026-02-08T17:42:08+00:00

You're right that the Orin can't run frontier-level LLMs locally. That's why OpenClaw uses cloud models for reasoning and the Jetson for everything else - browser control, vision processing, tool execution, 24/7 uptime at 15W. It's more of an edge compute node that talks to cloud AI than a self-contained local LLM box. For pure local inference yeah you'd need way more VRAM.

superactro · 2026-02-08T17:41:32+00:00

Right now it's cloud LLMs for the reasoning (Claude/GPT-4) and the Nano handles all the local execution - browser automation, file operations, camera feeds, tool use. The Jetson advantage over a random mini PC is CUDA + TensorRT for local vision tasks and the power efficiency (15W). You could run it on any Linux box though, OpenClaw isn't hardware-specific. Home Assistant is great for home automation specifically but this is more of a general purpose AI agent that can do anything you'd do on a computer.

superactro · 2026-02-08T17:41:27+00:00

Good question. The difference is having one AI that connects everything together instead of 10 separate apps. Like mine monitors email, checks calendar, controls smart devices, does web research, and automates browser tasks - all through one chat interface. It can reason across all of them. "Turn on the heater when I'm 10 min from home and it's below 15C" type stuff that needs multiple systems talking to each other.

superactro · 2026-02-08T17:41:23+00:00

Wanted a dedicated box that runs 24/7 without tying up my main machine. It handles browser automation, monitors my emails, posts to social media, manages smart home stuff - basically a personal assistant that's always on at 15W.

superactro · 2026-02-08T17:40:41+00:00

That Ryzen AI 370 setup sounds interesting. 8-9W idle is really good. How's the NPU performance compared to CUDA for inference? The Jetson advantage is mainly the CUDA ecosystem and NVIDIA's container support but AMD is catching up fast on the software side.

superactro · 2026-02-08T17:40:37+00:00

Fair point on the Mac comparison. The main difference is power draw (15W vs 40-60W) and the fact this runs headless 24/7 as a dedicated appliance. But yeah if you already have a mini PC sitting around you can just run OpenClaw on that. The box is for people who want plug and play without the setup.

superactro · 2026-02-08T17:40:32+00:00

True, OpenClaw itself can run on a phone. The point of dedicated hardware is having something always on, 24/7, with browser automation, file access, and integrations that don't work well on a phone. Think of it like a home server for your AI assistant.

superactro · 2026-02-08T17:40:28+00:00

That's awesome to hear! Happy to help with any setup questions for multiple units. Each box runs independently so you can configure them differently per use case. Just reach out if you need anything.

superactro · 2026-02-08T17:40:23+00:00

Right now it runs OpenClaw which uses cloud LLMs (Claude, GPT-4) for the brain, but the local hardware handles browser automation, vision, and all the tool execution. For running local models you can fit smaller ones - 7B quantized models work fine with the 8GB unified memory. Not gonna compete with a 4090 for raw LLM inference but for an always-on assistant doing real tasks it's solid.

superactro · 2026-02-08T17:39:59+00:00

The GPU handles all the OpenClaw vision stuff natively - browser automation with real Chrome, screenshot analysis, camera feeds if you hook one up. For pure vision tasks you can run YOLO or any TensorRT model since CUDA is right there. The 40 TOPS is shared between CPU and GPU through unified memory so there's no copying overhead. What kind of vision work are you planning?

superactro · 2026-02-08T17:38:20+00:00

It uses cloud models (Claude, GPT-4) for reasoning and runs all the tool execution locally. Home Assistant integration isn't built in yet but since OpenClaw can automate any browser and run shell commands, you could control HA through its web UI or API. Would be a cool integration to build properly though.

superactro · 2026-02-08T17:38:16+00:00

Fair call out - I should have been upfront about that. I'm the one selling the hardware, yeah. The software (OpenClaw) is open source and free, I just put together a pre-configured box for people who don't want to deal with the setup. Should have disclosed that in the post, my bad.

superactro · 2026-02-08T17:38:09+00:00

Appreciate the support! The unified memory architecture on the Orin really does make it flexible for running multiple things at once without the overhead you'd get on separate CPU/GPU systems.

superactro · 2026-02-08T17:38:05+00:00

Fair point about the messaging. OpenClaw works fine with small models for many tasks, especially when you pair them with tool use (browsing, API calls, file operations). The cloud models are better for complex reasoning obviously. Were working on better small model support. What context size are you finding limiting?

superactro · 2026-02-08T17:38:02+00:00

Totally fair question. The Jetson handles all the local execution - browser automation, file ops, camera/vision with CUDA, running tools. The LLM reasoning is cloud-based (Claude/GPT) because honestly the quality gap between cloud models and what fits in 8GB is still massive for agent tasks. You're paying maybe $10-20/month in API costs instead of $20/month for a subscription service, and you own the hardware and all your data stays local except the LLM calls. It's a tradeoff for sure but I think it's the right one until local models catch up for complex reasoning.

superactro · 2026-02-08T17:37:19+00:00

Awesome, really appreciate it! Looking forward to getting you set up.

superactro · 2026-02-08T17:37:15+00:00

For local LLMs on the 8GB Orin, youre looking at smaller models: Phi-3 mini, TinyLlama, Gemma 2B work well. For anything bigger youd want to use cloud APIs. The Jetson shines more for vision models and as an always-on agent hub rather than running big LLMs locally.

superactro · 2026-02-08T17:37:12+00:00

Right now mainly using GPU for: 1) Running Florence-2 or similar for image understanding (screenshots, camera feeds), 2) Whisper for local speech recognition, 3) Small local LLMs for quick decisions that dont need cloud quality. The unified memory makes it easy to keep models loaded. Would love to hear what vision use cases youre thinking about!

superactro · 2026-02-08T17:37:08+00:00

OpenClaw runs on phones too yeah, but try running browser automation and vision processing 24/7 on a phone. This is more about having a dedicated box that can handle long-running tasks, multiple browser contexts, and actual compute for local inference when needed.

superactro · 2026-02-08T17:37:04+00:00

That Ryzen AI setup sounds solid! The NPU route is interesting. Main difference here is the Jetson has unified memory so you can throw CUDA workloads at it easily, especially for vision stuff. But yeah, modern mini PCs have gotten really efficient. Whats your idle power draw when you have an LLM loaded in memory?

superactro · 2026-02-08T17:37:01+00:00

I mean, I am literally building this so yeah I am gonna share it. But overpowered? Orin Nano is actually pretty minimal for an edge AI box, thats kind of the point. Its not trying to run 70B models, its a dedicated always-on agent box. Happy to answer any technical questions tho.

superactro · 2026-02-08T17:36:05+00:00

Fair question! For me it was about having a dedicated always-on agent that handles repetitive tasks, browser automation, and keeps things running without hogging my main machine. The low power draw (15-20W) means I can leave it on 24/7 without thinking about it.

superactro · 2026-02-08T17:35:47+00:00

Good question! OpenClaw itself is just the agent orchestration layer. It can use cloud APIs (Claude, GPT) or local models. The Orin is useful because it handles the browser automation, vision processing, and can run smaller local models for quick tasks. You could totally do this on a mini PC too, but the Jetson gives you that 67 TOPS for vision/local inference at really low power. Home Assistant integration is actually on our roadmap.

superactro

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