How to Run OpenClaw on AgentVPS — Token-Based AI Hosting

Let me cut to it: if you're trying to run an AI agent that actually does something — controls a claw machine, handles vision-language-action tasks, runs 24/7 without you babysitting it — you need two things. You need OpenClaw for the model, and you need somewhere to host it that won't drain your bank account or lock you into some enterprise contract designed for Fortune 500 companies.

AgentVPS is a new platform that solves the hosting side. It's a GPU cloud built specifically for AI agents, it runs on Solana, and you pay with tokens instead of credit cards. It's weird. It's also genuinely interesting. And if you pair it with OpenClaw, you can have a fully autonomous claw-controlling AI agent running in under thirty minutes.

Here's exactly how to do it.

Why This Stack Matters

Most people trying to deploy AI agents right now are stuck in one of two painful loops:

Loop 1: They rent a GPU from a traditional cloud provider (AWS, GCP, Lambda Labs), spend two hours configuring CUDA drivers, pay $3/hour whether they're using the machine or not, and then forget to turn it off over the weekend. Monday morning: a $200 bill for an idle server.

Loop 2: They try to run inference locally on their gaming PC, which works great until they realize they need the machine running 24/7, their electricity bill spikes, and their girlfriend/roommate/spouse asks why the office sounds like a jet engine.

AgentVPS is trying to kill both loops. Pay-per-second billing (in tokens), pre-configured AI templates, and GPU instances that spin up in seconds. No driver hell. No surprise bills. No jet engine bedroom.

OpenClaw, meanwhile, is the model layer. It's an open-source 7B-parameter vision-language-action model specifically fine-tuned for claw machine automation and robotics tasks. It's not a general-purpose chatbot you're awkwardly repurposing. It was built for this. It understands spatial reasoning, real-time webcam feeds, and motor control outputs. You point it at a claw machine camera feed, and it tells you (or directly controls) where to move, when to drop, and how to grab.

Together: OpenClaw on AgentVPS gives you a dedicated, always-on AI claw agent running on serious GPU hardware for a few bucks an hour. Let's set it up.

Step 0: Understand the Token-Based Pricing

Before we deploy anything, let's talk about money — because this is where AgentVPS diverges from every other cloud provider.

There's no credit card input. No USD pricing. Everything runs on $AVPS, their native Solana token. You buy tokens, load them into your account wallet, and the platform deducts per-second as your VPS runs.

Here's what current pricing looks like:

GPU	VRAM	$AVPS / Hour	~USD Equivalent
RTX 4090	24 GB	45	$5.40
A100	40 GB	75	$9.00
H100	80 GB	150	$18.00

Storage runs about 0.01 $AVPS per GB/hour. Bandwidth is 0.005 $AVPS per GB egress. CPU and RAM are bundled — roughly 10% of GPU cost, basically negligible.

For running OpenClaw-7B, the RTX 4090 is the sweet spot. 24 GB VRAM is plenty for a 7B model, and at ~$5.40/hour, you're competitive with Lambda Labs and significantly cheaper than AWS for on-demand GPU. If you stake $AVPS tokens, you get a 15% discount, bringing the 4090 down to about $4.59/hour equivalent.

How to get $AVPS tokens:

Open your Solana wallet (Phantom or Backpack work best)
Go to Jupiter or Raydium
Swap SOL or USDC for $AVPS (contract: AVPSfJ9G5YHUwK1P8oHqHuPCuf9ZrUUk7Uz1bupX7V9P)
Current price floats around $0.12 per token

I'd recommend loading at least 500 $AVPS for your first session (~$60). That gives you about 11 hours of 4090 runtime, which is more than enough to deploy, test, and decide if you want to commit longer-term.

One thing I genuinely like about this model: you can't accidentally leave a server running and rack up a $900 bill. When your tokens run out, the instance pauses. You top up, it resumes. It's like a prepaid phone plan for GPU compute. Not elegant, but effective at preventing financial disasters.

Step 1: Create Your AgentVPS Account

Head to app.agentvps.com.

There's no email signup. No password. You connect your Solana wallet and that's your identity. Click "Connect Wallet," approve in Phantom, and you're in.

Once connected:

Click "Fund Account" in the dashboard
Transfer $AVPS from your wallet to the platform's escrow
Minimum top-up is 10 $AVPS (about $1.20 — low barrier, nice)

Your balance shows in the top-right corner. It updates in real time as instances run.

If you've never used a crypto-native platform before, this will feel foreign for about ninety seconds. After that, it's just a VPS dashboard. The Solana stuff is infrastructure plumbing — you don't need to think about it after the initial funding.

Step 2: Spin Up Your GPU Instance

From the dashboard, click "Launch VPS" and configure:

GPU: RTX 4090 (24 GB VRAM) — sufficient for OpenClaw-7B
CPU/RAM: 4 vCPUs, 32 GB RAM (default config works fine)
Storage: 100 GB NVMe (OpenClaw model weights are ~14 GB, plus OS and dependencies)
Region: Auto-select for lowest latency, or manually pick US-East if you're in North America

You'll see two deployment options:

Option A: One-Click Deploy (Recommended)

Search "OpenClaw" in the marketplace templates. AgentVPS added an official OpenClaw template in September 2024. Click Deploy.

This spins up a pre-configured image with:

Ubuntu 22.04
CUDA 12.x + cuDNN
Python 3.11
vLLM pre-installed
OpenClaw-7B model weights pre-downloaded
OpenWebUI frontend ready to go

Total spin-up time: about 2–4 minutes. Most of that is the GPU node initializing.

Option B: Custom Deploy (For Tinkerers)

If you want more control — or you're running a forked version of OpenClaw — launch a bare GPU instance and SSH in:

# SSH into your new VPS (credentials shown in dashboard)
ssh root@<your-vps-ip>

# Clone OpenClaw
git clone https://github.com/OpenClawAI/OpenClaw
cd OpenClaw

# Install dependencies
pip install -r requirements.txt

# Download model weights (if not pre-cached)
python scripts/download_model.py --model OpenClaw-7B

# Launch inference server with vLLM
python -m vllm.entrypoints.openai.api_server \
  --model OpenClaw-7B \
  --host 0.0.0.0 \
  --port 8000 \
  --gpu-memory-utilization 0.9 \
  --max-model-len 4096

Custom deploy takes about 10–15 minutes including model download. The one-click template saves you this time, but the custom route gives you full control over versions and configuration.

Either way, once the server is running, you'll have an OpenAI-compatible API endpoint at http://<your-vps-ip>:8000.

Step 3: Verify OpenClaw Is Running

Quick health check:

curl http://<your-vps-ip>:8000/v1/models

You should see:

{
  "data": [
    {
      "id": "OpenClaw-7B",
      "object": "model",
      "owned_by": "openclaw"
    }
  ]
}

Now test an actual inference call:

curl http://<your-vps-ip>:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "OpenClaw-7B",
    "messages": [
      {
        "role": "user",
        "content": "Analyze this claw machine state: claw is at position (120, 85), target plush is at (200, 150), claw height is 40cm. What move sequence do you recommend?"
      }
    ],
    "max_tokens": 512
  }'

OpenClaw should return a structured action sequence — something like move right 80 units, forward 65 units, lower claw, close grip, with confidence scores for the grab.

If you get a response, you're live. Your AI claw agent has a brain. Now let's give it eyes and hands.

Step 4: Connect Your Claw Machine Feed

OpenClaw's killer feature is its /api/control endpoint, which accepts real-time webcam input and outputs motor control commands. Here's the basic integration:

import requests
import cv2
import json
import time

OPENCLAW_URL = "http://<your-vps-ip>:8000/api/control"
CAMERA_INDEX = 0  # Your webcam / IP camera

cap = cv2.VideoCapture(CAMERA_INDEX)

while True:
    ret, frame = cap.read()
    if not ret:
        break
    
    # Encode frame
    _, buffer = cv2.imencode('.jpg', frame)
    
    # Send to OpenClaw for analysis
    response = requests.post(
        OPENCLAW_URL,
        files={"image": ("frame.jpg", buffer.tobytes(), "image/jpeg")},
        data={"mode": "autonomous", "confidence_threshold": 0.7}
    )
    
    action = response.json()
    print(f"Action: {action['command']} | Confidence: {action['confidence']}")
    
    # Send action to your claw controller
    # send_to_claw_controller(action)
    
    time.sleep(0.1)  # 10 FPS control loop

For physical claw machine integration, you'll need a hardware controller (Arduino/Raspberry Pi) bridging OpenClaw's commands to the actual motors. Check the Claw Mart shop for compatible controllers and claw machine kits that work out of the box with OpenClaw's API format.

For virtual/simulated claw games, OpenClaw includes a built-in simulator at http://<your-vps-ip>:8000/simulator — useful for testing before you hook up to real hardware.

Step 5: Set Up Telegram Notifications (Optional but Recommended)

If your claw agent is running autonomously, you probably want notifications when it grabs something (or fails spectacularly). Here's a quick Telegram bot integration:

import requests

TELEGRAM_BOT_TOKEN = "your-bot-token"
TELEGRAM_CHAT_ID = "your-chat-id"

def notify(message):
    requests.post(
        f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage",
        json={"chat_id": TELEGRAM_CHAT_ID, "text": message}
    )

# In your control loop, after each grab attempt:
if action["command"] == "grab_complete":
    if action["success_probability"] > 0.8:
        notify(f"🏆 Successful grab! Confidence: {action['confidence']:.0%}")
    else:
        notify(f"❌ Missed. Repositioning. Confidence was: {action['confidence']:.0%}")

Set up a Telegram bot via @BotFather, grab your token and chat ID, drop this into your control script. Now you get real-time updates on your phone while your agent works.

Step 6: Keep It Running (Always-On Configuration)

For a 24/7 claw agent, you want the inference server to survive reboots and crashes:

# On your AgentVPS instance
sudo tee /etc/systemd/service/openclaw.service > /dev/null <<EOF
[Unit]
Description=OpenClaw Inference Server
After=network.target

[Service]
Type=simple
User=root
WorkingDirectory=/root/OpenClaw
ExecStart=/usr/bin/python -m vllm.entrypoints.openai.api_server \
  --model OpenClaw-7B \
  --host 0.0.0.0 --port 8000 \
  --gpu-memory-utilization 0.9
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl enable openclaw
sudo systemctl start openclaw

Now OpenClaw restarts automatically if it crashes or the VPS reboots.

For token management, set up auto-refill in the AgentVPS dashboard. You specify a minimum $AVPS balance threshold, and when your account drops below it, it pulls from your connected wallet automatically. No more instances pausing at 3 AM because you forgot to top up.

Cost Breakdown: What Does Always-On Actually Cost?

Let's do the math for running OpenClaw 24/7 on a 4090:

Item	Cost/Hour ($AVPS)	Cost/Day	Cost/Month
RTX 4090 GPU	45	1,080	32,400
Storage (100 GB)	1	24	720
Bandwidth (~5 GB/day)	0.025	0.025	0.75
Total	46.025	~1,104	~33,121

At $0.12/AVPS, that's roughly $132/day or $3,975/month for an always-on claw agent with dedicated GPU.

That sounds like a lot until you compare:

AWS p3.2xlarge (V100): ~$2,200/month + data transfer + storage = ~$2,500+
Lambda Labs A10: ~$2,160/month (often unavailable)
Your own RTX 4090 rig: ~$2,000 hardware + $50/month electricity + your time maintaining it

AgentVPS is pricier than Lambda's spot pricing but cheaper than AWS on-demand, and you don't deal with availability issues or instance preemption. For production claw agents that need to be running when a customer walks up to the machine, reliability matters more than saving $200/month.

Budget option: Run OpenClaw only during peak hours (say, 12 hours/day). That cuts your cost to ~$1,990/month. Configure your claw machine to display an "AI Agent Sleeping" screen during off-hours. This is what most Claw Mart operators do.

If you lock in for a month, AgentVPS gives you a 20% discount on token burn rate, bringing the always-on monthly cost down to about $3,180. Staking gets you another 15% off. Stack both and you're at roughly $2,700/month — now we're genuinely competitive with the cheapest options available.

What Makes This Different From Just Running Ollama on a DigitalOcean Droplet

Fair question. You could rent a basic VPS, install Ollama, and run a general-purpose model. Here's why that's a bad idea for claw automation specifically:

DigitalOcean doesn't offer GPUs. You'd be running CPU inference. OpenClaw-7B on CPU takes 15–30 seconds per inference. Your claw machine needs 100ms response times. Game over.
General-purpose models don't understand claw physics. OpenClaw was specifically fine-tuned on claw machine datasets — gripper mechanics, prize positioning, depth estimation from monocular cameras. Asking a general LLM to do this is like asking a poet to do your taxes. Technically possible, practically useless.
The token model actually aligns incentives for this use case. Claw machines have variable usage. Some days nobody plays, some days there's a line. Pay-per-second means you're not paying for idle time during slow Tuesday afternoons. Your costs scale with actual usage.

Where Claw Mart Fits In

If you're reading this on the Claw Mart blog, you probably already know we sell claw machines, parts, and accessories. Here's the relevant context:

OpenClaw-compatible claw machines: We carry machines with pre-wired API controller boards that plug directly into OpenClaw's control endpoint. No soldering. No custom firmware. Plug in the USB cable, point it at your AgentVPS instance, done.
Camera kits: Wide-angle USB cameras optimized for overhead claw machine mounting. OpenClaw performs best with 120° FOV overhead perspective. We sell the exact camera models the OpenClaw team used for training data.
Controller boards: Arduino-based motor controllers with OpenClaw's serial protocol pre-flashed. Compatible with most standard claw machine motor configurations (3-axis + grip).

Browse the Claw Mart shop for the full lineup. If you're setting up a commercial claw machine with AI-assisted gameplay, we have bundle deals that include the camera, controller, and a prepaid $AVPS token package to get you started.

Honest Assessment: Should You Actually Do This?

AgentVPS is early. It launched in 2024, it's running ~50 nodes globally, and the token price fluctuates. The 99.5% uptime is good but not enterprise-grade. The Solana wallet requirement will confuse non-crypto-native users. The documentation is improving but has gaps.

But it's also genuinely useful. The one-click OpenClaw template works. The per-second billing is honest. The GPU availability is better than Lambda Labs on most days. And if you're already in the Solana ecosystem, the token-based flow feels natural.

OpenClaw itself is impressive for what it is — a purpose-built vision-language-action model for a very specific robotics task. It's not trying to be everything to everyone. It's trying to be the best at controlling claw machines, and from my testing, it's significantly better than any hacky prompt-engineering approach on a general model.

My recommendation: try the free tier first. AgentVPS gives you 1 hour of free VPS time. That's enough to deploy OpenClaw, run the simulator, and see if the response quality meets your needs. If it does, load up 500 $AVPS and run a real test with your hardware.

Next Steps

Get $AVPS tokens: Swap on Jupiter — you need a Solana wallet like Phantom first
Sign up at app.agentvps.com: Connect wallet, fund account
Deploy OpenClaw: One-click template, 3 minutes to live
Test with the simulator: http://<your-ip>:8000/simulator
Get hardware from Claw Mart: Camera kit + controller board for physical integration
Join the OpenClaw GitHub community: File issues, contribute, check the model card for fine-tuning your own variants

The AI-powered claw machine isn't a future thing. The models exist, the hosting exists, and the hardware is plug-and-play. The only question is whether you're going to set it up this week or keep reading about it.

Go grab something. 🪝