I’ve looked into SillyTavern, I can’t set it up. I talked with chatgpt and huggingchat, didn’t get anywhere. I found some solutions, but they’re paid. Huggingface has an api but using it requires learning python.
I have a basic Huggingface subscription.
What is the best solution to my problem?
Downloads those free models from website to your computer, and use your base enviorments to run them.
It’s quite difficult to use models on Hugging Face “today” for “remote” inference “at no cost.” So, I agree that it’s more convenient to use tools like Ollama (CUI) or LM Studio (GUI) locally rather than remotely…
As for the GUI, the experience is generally the same whether you’re working locally or remotely. SillyTavern and many other frameworks allow you to use models from both local and remote sources (including major commercial AI providers).
If remote, for example:
The best solution for you today is this:
Use OpenRouter Chat Playground as your primary GUI.
Then, if you want a cleaner desktop experience, use Jan connected to OpenRouter.
Keep Hugging Face model widgets and public Spaces as a fallback for specific models that are already hosted there.
If later you need something a bit more stable or cheaper per token than OpenRouter’s free tier, use DeepInfra or Groq as the backend. (OpenRouter)
That is the lowest-friction setup that satisfies your actual requirements:
Your original question sounds simple:
“How do I run the models under
huggingface.co/modelsremotely?”
The reason this becomes confusing is that Hugging Face’s model list is a catalog, not a promise that every model is directly runnable through a simple hosted GUI.
Hugging Face’s own docs say model-page widgets appear only when at least one Inference Provider is serving that specific model and task. Their docs also point users to widgets, the Inference Playground, and provider filters to find models that are actually available for hosted inference. So the hard part is not learning Python. The hard part is that many Hub repos are just repos unless somebody is already hosting them for inference. (Hugging Face)
That is why many “easy” Hugging Face solutions feel broken or incomplete. They are trying to turn a model repository into a turnkey hosted app. That only works for the subset of models that are already provider-backed. (Hugging Face)
Hugging Face does have hosted inference, but their current Inference Providers pricing is fundamentally pay-as-you-go, and the free monthly credits are very small. Their pricing docs say they charge the same rates as the provider with no markup, and their pricing page also shows dedicated Inference Endpoints starting at $0.033/hour. That is fine for developers and production use. It is not the cleanest “easy, cheap, no-code daily driver” path for an end user who just wants to chat with open models remotely. (Hugging Face)
So the honest answer is:
Do not build your whole plan around Hugging Face’s own hosted inference layer unless you are okay with pay-as-you-go and model-by-model availability constraints. (Hugging Face)
This is the best fit for you.
Why:
OpenRouter’s docs say the easiest way to try free models is the Chat Playground. Their Free Models Router guide says openrouter/free is the simplest way to get free inference and automatically selects an available free model that supports the features your request needs. Their pricing page says the free plan currently has 50 requests/day and 20 requests/minute, while pay-as-you-go has no minimums and no lock-in. (OpenRouter)
Why this matters for you:
You do not actually need “the Hugging Face API.” You need a hosted open-model service with a good GUI. OpenRouter gives you that directly. It removes the hardest parts:
This is the cleanest “just let me use open models remotely today” solution. (OpenRouter)
It is not a mirror of the entire Hugging Face Hub. It gives you access to OpenRouter’s catalog, not all HF repos. Free use is also rate-limited. (OpenRouter)
But for your actual use case, that is acceptable. You want something usable, not perfect.
If you want something that feels like an actual app instead of a browser tab, this is the best desktop path.
Jan’s docs have a dedicated OpenRouter integration page. Jan says it supports OpenRouter directly, and the setup is straightforward: create an OpenRouter key, open Jan, go to Settings → Model Providers → OpenRouter, paste the key, then choose a model and chat. Jan’s QuickStart says installation is simple on Mac, Windows, and Linux. (jan.ai)
Why this is strong:
Why I do not put it first:
So the sequence I would recommend is:
That gives you the least friction. (OpenRouter)
Groq is not my first choice for pure simplicity, but it is an excellent second provider to keep ready.
Groq’s docs say the API is OpenAI-compatible, with base URL https://api.groq.com/openai/v1. Their overview says Groq is “Fast LLM inference, OpenAI-compatible.” Their pricing page says you can get started for free and upgrade as needed. Jan also has a dedicated Groq integration page. (GroqCloud)
Why Groq matters for you:
Why I still rank it behind OpenRouter:
So I would treat Groq like this:
This is the best way to use Hugging Face without turning it into a project.
Hugging Face’s model inference docs say:
But the widget docs also make the key limitation clear: widgets are only there when hosted inference is actually available for that model and task. (Hugging Face)
So the right way to use Hugging Face is:
That single decision rule will save you a lot of frustration. (Hugging Face)
It gives you access to Hugging Face’s ecosystem when the easy hosted path already exists.
It does not let you run arbitrary Hub repos remotely through a universal GUI.
That is the central limitation in your problem.
If later you decide that the free tiers are too tight, DeepInfra is one of the cleanest cheap upgrades.
DeepInfra’s docs say they provide an OpenAI-compatible API for all LLM and embeddings models at https://api.deepinfra.com/v1/openai. Their pricing page says they use pay-for-what-you-use pricing with no long-term contracts or upfront costs. Their docs also say they provide 100+ models and additional non-chat tasks on the native API. (Deep Infra)
Why it is relevant:
Why it is not the first answer:
So I would use DeepInfra only after you have already decided your free path works and you want a cheap serious backend. (Deep Infra)
Too tied to pay-as-you-go and model-by-model provider availability for your budget-sensitive, no-code goal. (Hugging Face)
These are for dedicated deployments, not for casual easy use. Hugging Face’s pricing page shows them starting at $0.033/hour. That is a different category of product. (Hugging Face)
That is the trap. Hugging Face’s own docs do not support that expectation. Widgets and provider-backed availability are the gate. (Hugging Face)
If a tool makes you think about adapters, provider configs, middleware, base URLs, or manual prompt-formatting before you can even chat, it is already drifting into “software project” territory for your case.
Use this:
Use OpenRouter Chat Playground. (OpenRouter)
Use Jan + OpenRouter. (jan.ai)
Use Jan + Groq. (jan.ai)
Use widgets or public Spaces only when they already exist for that model. (Hugging Face)
Upgrade to DeepInfra before you think about dedicated endpoints or building your own stack. (Deep Infra)
If I had to choose the best practical setup for you today, I would do this:
Use Hugging Face for:
Do not use it as the center of your remote-inference setup unless a specific model is already hosted and easy there. (Hugging Face)
The best solutions for you today are:
And the most important truth is this:
There is no simple no-code GUI that turns the entire Hugging Face model catalog into instantly runnable remote models.
The easiest workable solution is to use a service built for hosted model access first, then use Hugging Face only where Hugging Face already provides the hosted layer. (Hugging Face)