OpenClaw Survivor Guide: Stop Fighting CUDA, Start Shipping

OpenClaw Survivor Guide: Stop Fighting CUDA, Start Shipping

A reality check on running OpenClaw. Why fighting with local hardware is a trap, and how to use the 'Just Work' method with Api.Airforce.

Choose how you want to lose time. You can spend a weekend fighting NVIDIA drivers, or you can be up and running in 5 minutes.

⚠️ Reality Check

PATH A: CLOUD API (Recommended)

  • Setup time: 5 mins
  • Hardware: Any laptop
  • Cost: Cheap (Usage based)
  • Stability: Operationally boring

PATH B: LOCAL HARDWARE

  • Setup time: 1-4 hours (if lucky)
  • Hardware: 16GB+ RAM / GPU
  • Cost: Electricity + Sanity
  • Stability: Experimental

Path A: The "Just Work" Method

Why fight with drivers? We've already done the hard work. Here is the shortcut to sanity:

1. Install OpenClaw

npm install -g openclaw@latest

2. Configure .env

Create a .env file in your project root. Point it to Api.Airforce to access premium models like DeepSeek R1, Claude 4.5, and more without the hardware headache.

# Use Api.Airforce for instant access
LLM_PROVIDER="openai"
LLM_BASE_URL="https://api.airforce/v1"
LLM_API_KEY="your-airforce-key"
LLM_MODEL="deepseek-reasoner" # or claude-opus-4.5-uncensored

3. Launch

openclaw start

Why I switched from Local to API

"I tried Path B first and wasted a whole weekend fighting drivers. This costs billable hourly rates, which is cheaper than my hourly rate for debugging CUDA errors."

The Horror of Path B (Local)

If you have less than 16GB RAM, turn back now. Your system will freeze. Even with an RTX 3090 (24GB), you might hit OOM errors.

[2026-02-01 14:24:43] ERROR: CUDA out of memory. Tried to allocate 128.00 MiB (GPU 0; 23.99 GiB total capacity; 23.10 GiB already allocated; 0 bytes free)

If you choose this path, you are choosing to debug physics. Save yourself the trouble. Rent the metal and start shipping with Api.Airforce.

Ready to Stop Debugging?

Get your API key and launch OpenClaw in 5 minutes.

Go to Dashboard →