No Inference Provider Available!

Hello,

I’m trying to connect Hugging Face models to n8n, but I keep getting the error: “No Inference Provider available for model x.” I’ve tested several models and receive the same issue each time.

My goal is to integrate a model trained specifically for medical image analysis (e.g., [MedGemma]). Is there a supported way to connect it to n8n?

I also tried using a medical VLM via Ollama, but I get an error of: unable to analyze medical images (only answers medical question, which should not be the case as the model supports both text and image inputs).

I am using n8n locally.

Any guidance would be appreciated.

Hi @Rana_Benmahmoud The error message “No Inference Provider Available” is not a bug of MedGemma but a limitation. The n8n node’s Hugging Face inference service does not support many specialized models like MedGemma.

Two main way forward, supported by us.

It is recommended to pull and run MedGemma locally using Ollama. Then connect n8n to MedGemma using the OpenAPI-Compatible API credentials and set the endpoint to http://localhost:11434/v1. It goes directly against Hugging Face service.

Try checking if your model is available on any services such as Fal.ai, Replicate, and Novita. If that’s the case, you can use the HTTP Request node to link n8n to them. Larger and known models work with hugging face but some specially trained or different small models like this one gives errors, hope this helps.

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Hey guys!

To add some clarity, n8n doesn’t run models by name alone. Every model must be accessed through a supported inference provider (such as OpenAI-compatible APIs, Ollama, or external inference services).

When a model is not exposed by one of these providers, n8n cannot execute it directly, which is why the “No Inference Provider available” message appears. In these cases, the supported approach is to either use a provider that already serves the model or connect to a custom inference endpoint via the HTTP Request node, as outlined in the documentation.

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Hi @Rana_Benmahmoud, welcome!

I connected it as an OpenAI-compatible API using OpenAI credentials with base URL:

https://router.huggingface.co/v1

It works fine on my end:

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Thank you Mohamed, were you able to chat with the model? make it analyze medical image? Or answer medical questions?

I will give this a try and let you know, thank you!

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It depends on the model you’re using and whether it supports vision,

If it does, enable the option “Automatically Passthrough Binary Images”:

After that, the model should be able to see the image:

I’m talking about the MedGemma model

This specific model isn’t deployed by any inference provider:

So, for a normal Hugging Face account, it won’t be available unless you deploy it,
If you’ve already deployed it, you should see it in the list of available models on your side,

here, in my case it’s not available, so I get this error:

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So once deployed, I’ll be able to use it correct?

So once I connect my model with Ollama, I can use it correctly right?

I hope so :slight_smile:

I didn’t feel comfortable saying yes since I haven’t tried it myself!

Have you personally tried it, especially given that it requires making a payment?

I hope your advice is based on real experience, because it could cause someone to pay for something that turns out not to be correct..

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Hi @mohamed3nan :sweat_smile: Sorry if that felt unprofessional or incorrect, in my college project i have used mlgawd/navarasa-2b-2.0-cyberdost · Hugging Face this model and hosted this on AWS it works fine for me, like i understand your concern to be more professional here. I would consider this from now on, my bad. I will delete that for now.

For the context of the asked question: If you want you can self host that model on AWS which is a bit technical but would get the job done, i do not recommend that because your self hosted model would not be updated and will not get any future release, so for now i recommend using OpenAI’s model cause they are very largely trained and with right system prompt and AI prompt would really get the job done without any much overhead of updating in future. Hope this helps.