Are My PC Specs Sufficient for Running n8n via Docker?

Below is an analysis based on n8n's documentation and Docker recommendations:

n8n Resource Demands

• CPU:
  n8n isn’t very CPU intensive. Even small instances are sufficient in most cases because the workflows generally rely more on memory. An Intel Core i5-1135G7 should be more than adequate.

• RAM:
  While n8n Cloud at idle might use around 100 MB, even if you run several workflows with additional nodes (e.g., Code node for pre-/post-processing) you’d still be well within safe limits with 16 GB RAM. For context, the suggested base resource requirements for self-hosting n8n are far lower (e.g., 320 MB RAM for a starting configuration).

• Storage:
  A 512GB SSD provides the necessary speed and capacity for both Docker and the database (SQLite or PostgreSQL) that n8n uses.

Docker Considerations

• Running n8n in Docker is recommended for a clean environment and ease of database management.
• Ensure you persist data using Docker volumes (for example, mounting to /home/node/.n8n) to safeguard your credentials and workflow data.

GPU Considerations

• n8n itself does not utilize GPU resources for its typical automation tasks.
• The included Intel Iris Xe Graphics should not impact the performance of n8n. GPU utilization generally comes into play for applications specifically designed for parallel computing or heavy graphical processing (like CUDA-enabled workloads), which is not the case for standard n8n workflows.

Conclusion

Your PC configuration—with an adequate CPU, ample RAM, fast SSD storage, and an integrated GPU—is more than capable of running lightweight automation workflows and AI-related tasks in n8n when using Docker. As long as your workflows themselves remain light (and you persist your data correctly via Docker volumes), you should experience smooth performance without any significant issues.

Feel free to ask if you need any more details on setting up Docker or optimizing your n8n instance!