OpenClaw on the ROG Ally: turn your handheld into a fully local AI assistant

OpenClaw (formerly known as Moltbot, formerly known as Clawdbot) has taken the world by storm: with 100,000+ Github stars and 2 million visitors in just a few days, this always-on self‑hosted agentic AI assistant can do just about anything: proactively message you with daily briefings, run shell commands, manage your inbox, automate tasks inside apps like WhatsApp or Slack, and even place real phone calls to complete errands. In other words: it actually does things, rather than just chat with you. And since it runs locally on your own hardware rather than in the cloud, developers everywhere are digging up hardware to run it on.

Due to this always-active nature, it makes sense to have a separate machine dedicated to it — making it tempting to use old hardware you have lying around. But the older the hardware, the less powerful and speedy your AI assistant will be (and the more power hungry). If you can get something newer, you’ll be better off — especially if it’s smaller and more efficient. A lot of tinkerers have turned to mini PCs for OpenClaw, an while ASUS has plenty of those, there’s another device that seems perfectly suited for the task: the ROG Ally.

Why the ROG Ally Is a Perfect Match for OpenClaw’s Local-First Design

As a portable Windows handheld, the Ally is ultra-compact, so you can stash it anywhere. It has a built-in screen, so you don’t need to connect an extra monitor when tinkering with your assistant (just a mouse and keyboard). It also has low idle power usage, while still sporting an AMD Ryzen Z1 Extreme processor with a powerful GPU built in. And its unified memory structure is particularly handy for LLM work: VRAM is often the bottleneck for AI applications, and the Z1 Extreme lets you customize how much memory is assigned to VRAM. Plus, many handheld enthusiasts have already upgraded to the latest and greatest ROG Xbox Ally X, there are bound to be a few ROG Ally handhelds sitting unused on shelves. It’s the perfect tinkering device for running OpenClaw on Windows.

(You could, of course, throw even more power at the bot if you have a Strix Halo-based device like the ROG Flow Z13 around, but that’s more likely to be your primary tablet, which may prevent it from being ideal for these purposes. But hey, if you’ve got one to spare, have at it!)

There’s one other big advantage to this approach: while running OpenClaw on your main Windows computer may be tempting, a dedicated device you can stash out of sight is better for security. You don’t have to run OpenClaw on a machine that already has access to your entire digital life; instead, you can run it on a small, dedicated device with a separate OS account, limited privileges, and connect it to a guest WiFi network to keep things contained.  You might even choose to create secondary or sandboxed email accounts, use an intermediary service like Zapier, or add other limitations to the scope of OpenClaw’s permissions. Much has already been written about OpenClaw’s security, so we recommend you look up best practices before moving forward — but having a separate device dedicated to the service is a good start.

Selecting a local AI model for the ROG Ally or Ally X

If you want to run AI locally, you’ll have to make some careful decisions about what model to use — and much of that decision is based on the hardware you have on hand.

The original 2023 ROG Ally features 16GB of RAM, 8GB of which can be dedicated to VRAM. If you’re looking to run a local GPU‑accelerated LLM without relying on cloud APIs and subscriptions, you’ll want to start with an efficient model that’s friendly to the hardware. I’d begin with something like Gemma3 4B or DeepSeek‑R1 7B and work your way up from there. Models like Mistral 7B can work well too if you use Q4 quantization. Ultimately, your use case — and how much speed you need — will determine which models perform best for you.

If you happen to have an ROG Ally X with 24GB of RAM, then you can allocate up to 16GB to VRAM, giving you the ability to run larger models (or the same models at higher precision).

If you’re already experienced in this arena, you probably have a basic idea of what models might fit your use case — but you may need to experiment with different models, parameter levels, and quantization to find what's best for you. Everyone’s needs will be different!

Get out there and tinker!

Convinced? Time to go out there and make it happen. Fire up your ROG Ally, set it up with a new account as needed, and from there, it’s just like running local AI on any other Windows machine, with some tiny tweaks specific to the Ally:

• Make sure the Ally is plugged in and set to Turbo Mode in the Command Center
• Install the Windows Subsystem for Linux (WSL) or your command line tool of choice
• Install Ollama and pull your model(s) of choice
• Adjust your VRAM in Armoury Crate under Settings > Performance > GPU Settings to match your model’s needs
• Install OpenClaw and start the service

How much VRAM you need will depend on the model you plan to run — remember, you’ll need enough system RAM too, so you don’t necessarily want to max out your VRAM. Depending on the level of quantization, you might be best off running 6-7GB of VRAM here. Don’t be afraid to play around with this once you’re set up to find the right balance.

Once OpenClaw is running, you can start playing around with real-world tasks like automating a morning routine or summarizing your inbox — these are the kinds of agentic behaviors that made the project go viral.