this post was submitted on 07 Apr 2026
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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.

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[–] reptar@lemmy.world 12 points 4 days ago (2 children)

Happy to answer questions about the setup.

Tell me about the hardware, please and thank you.

[–] clifmo@programming.dev 1 points 3 days ago

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

[–] mental_block@lemmy.wtf 3 points 4 days ago (1 children)

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 points 4 days ago (1 children)

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 points 4 days ago (2 children)
[–] CCMan1701A@startrek.website 1 points 2 days ago

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 points 4 days ago

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.

[–] irmadlad@lemmy.world 7 points 4 days ago

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 points 4 days ago (1 children)
[–] captcha_incorrect@lemmy.world 12 points 4 days ago

https://n8n.io/

It is an automation platform with a selfhosted tier.

[–] warmaster@lemmy.world 2 points 4 days ago (1 children)

Has anyone tried ActivePieces? How does it compare?

[–] irmadlad@lemmy.world 1 points 3 days ago

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 points 4 days ago

Ollama has long history of exploits. PLease do not feed anything which come from outside to it.