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Troubleshooting

"Device too slow: X tok/s (minimum: 5 tok/s)"​

Your GPU is not being used. node-llama-cpp is running on CPU, which is too slow for the network.

NVIDIA (Linux/Windows):

# Both of these should work:
nvcc --version
nvidia-smi

If either fails, install the CUDA toolkit. On Ubuntu: sudo apt install nvidia-cuda-toolkit. On Windows, download from developer.nvidia.com.

AMD (Linux/Windows): Vulkan should be auto-detected. If not, install Vulkan drivers:

# Ubuntu
sudo apt install mesa-vulkan-drivers
# Verify
vulkaninfo | head

Apple Silicon: Metal auto-detects on Apple Silicon. If performance is unexpectedly low:

  • Check Activity Monitor → GPU tab for GPU usage
  • Make sure you're running native arm64 Node.js: node -p "process.arch" should output arm64
  • Free up RAM — close other apps

"Connection error: Invalid authentication token"​

  • Your worker token may be expired or invalid
  • Generate a new one from c0mpute.ai/worker → Native Worker → Get Worker Token
  • Make sure you're logged in to the same account that generated the token
  • Tokens start with cwt_ — make sure you copied the full string

Model download fails​

The model (~9GB) downloads from HuggingFace on first run.

  • Check disk space: you need ~10GB free in ~/.c0mpute/models/
  • Check internet: try curl -I https://huggingface.co to verify connectivity
  • Retry: HuggingFace occasionally has temporary issues. Just run the command again.
  • Behind a proxy? Set HTTPS_PROXY environment variable

Worker disconnects frequently​

  • Check your internet stability — packet loss or high latency causes disconnects
  • The worker auto-reconnects after a disconnect, but you lose any in-progress jobs
  • If using WiFi, try a wired connection
  • Check if your firewall is blocking WebSocket connections

Low tok/s on Windows​

This is the most common issue. Native Windows CUDA support is flaky with node-llama-cpp.

Solution: use WSL.

  1. Install WSL2: wsl --install
  2. Install Node.js and CUDA toolkit inside WSL
  3. Run the worker from WSL terminal

See the Windows setup guide for full instructions.

Key point: nvidia-smi should work inside WSL, not just in PowerShell. CUDA needs to be installed in the WSL environment.

Worker starts but gets no jobs​

  • Check that your worker benchmarks above 5 tok/s (minimum threshold)
  • Make sure you're running the right model (Max tier only runs on native workers)
  • The network matches jobs based on availability — if many workers are online, jobs are distributed
  • Check the worker page at c0mpute.ai/worker for network status