Been running n8n with Ollama for a few months now for work automation. Wanted to share what I've learned since it's not super well-documented.
The setup is just Docker Compose with n8n + Ollama + Postgres. n8n's HTTP Request node talks directly to Ollama's REST API — no custom nodes needed.
What I'm running:
- Email digest every morning (IMAP → Ollama → Slack)
- Document summarization (PDF watcher → Ollama → notes)
- Lead scoring from form webhooks
Zero API costs, everything stays on my server. If anyone wants the workflow templates I have a pack: https://workflows.neatbites.com/
Happy to answer questions about the setup.
15 Comments
reptar@lemmy.world · 12 pts · 123d
Tell me about the hardware, please and thank you.
mental_block@lemmy.wtf · 3 pts · 122d
Piggybacking too as I am considering the same. Please OP and thank you.
And what model class are you using? Lightweight (2B), reasonable ~10B or above 32B?
Do they load fast?
I had a look at NetworkChucks setup and don't think I can afford an overpowered rig in this economy. Depending on the rig, may have to wait >20s for a prompt answer.
Thank you again!
frongt@lemmy.zip · 3 pts · 122d
I was playing with ministral-3 3b on a 3060. It loads pretty quick, but response generation is a bit slow. It starts responding nearly instantly once the model is loaded (which is also quick), but for long responses (~5 paragraphs) it may take 15-20 seconds for the whole thing.
surewhynotlem@lemmy.world · 4 pts · 122d
Cries in 1070
CCMan1701A@startrek.website · 1 pts · 121d
I run llms using a 780m you'll be fine. I get pretty close to 10 tokens a second for larger 20B+ models.
frongt@lemmy.zip · 1 pts · 122d
I'd still give it a shot. A quick check of benchmarks suggests it's not that much slower. I don't know if that extends to ML computation though.
clifmo@programming.dev · 2 pts · 121d
I do something similar with the base model m4 Mac mini. It's my inference box right now, it handles Immich ML, photo prism AI, and runs Ollama talking to a small web app I call to summarize things. It's summaries are shit. The bigger the model, the more it hallucinates. So I settle for 1B and 4th grade responses
irmadlad@lemmy.world · 7 pts · 123d
I really like n8n. It appeals to my visual sense which makes up for a lot of hard programming experience. I don't run it full with the AI aspect. Not because I have some agenda against AI, but that my equipment is not good enough to run AI efficiently. I use it for a lot of automation around the lab.
jeena@piefed.jeena.net · 6 pts · 123d
What is n8n?
captcha_incorrect@lemmy.world · 12 pts · 123d
https://n8n.io/
It is an automation platform with a selfhosted tier.
coffelov@lemmy.ml · 6 pts · 123d
What model do you mostly use for those tasks
tofubl@discuss.tchncs.de · 4 pts · 122d
I'll piggyback onto this question: With the models you use, how do they compare to current models from the big players?
warmaster@lemmy.world · 2 pts · 122d
Has anyone tried ActivePieces? How does it compare?
irmadlad@lemmy.world · 1 pts · 122d
Briefly. I didn't like it as much as I like n8n. Perhaps it was not suitable to my use case. I hear a lot of good things about ActivePieces tho. You know, give it a spin and see if it gehaws with your flow. From what I understand, both can acomplish about the same. I think ActivePieces is geared more towards cloud deployments whereas n8n keeps things local.
TheHolm@aussie.zone · -1 pts · 122d
Ollama has long history of exploits. PLease do not feed anything which come from outside to it.