Quick post about a change I made that’s worked out well.
I was using OpenAI API for automations in n8n — email summaries, content drafts, that kind of thing. Was spending ~$40/month.
Switched everything to Ollama running locally. The migration was pretty straightforward since n8n just hits an HTTP endpoint. Changed the URL from api.openai.com to localhost:11434 and updated the request format.
For most tasks (summarization, classification, drafting) the local models are good enough. Complex reasoning is worse but I don’t need that for automation workflows.
Hardware: i7 with 16GB RAM, running Llama 3 8B. Plenty fast for async tasks.
In general, you take the model size in billions of parameters (eg: 397B), divide it by 2 and add a bit for overhead, and that’s how much RAM/VRAM it takes to run it at a “normal” quantization level. For Qwen3.5-397B, that’s about 220 GB. Ideally that would be all VRAM for speed, but you can offload some or all of that to normal RAM on the CPU, you’ll just take a speed hit.
So for something like Qwen3.5-397B, it takes a pretty serious system, especially if you’re trying to do it all in VRAM.