Hugging Face is a wonderful platform for sharing AI models, datasets, and knowledge. However, it can sometimes feel overwhelming for newcomers—and even experts—to stay up to date with all the latest news and amazing capabilities. In previous posts, I discussed a few features I find very valuable for AI developers in general.
This time, we’ll focus on a specific but fundamental part of any AI solution: inference. When you find a great model on Hugging Face that you want to use, the immediate questions are: How can I use it? and How much will it cost?
Many of you might already have experimented with models in Hugging Face Spaces—such as Llama 3B, Flux Schnell, and thousands of others—where you can simply type a question into the Space and start using it right away. That’s a great way to explore a model’s abilities. But actually using and integrating a model into your own application is another story.
Some people may also be familiar with the Transformers library, which can pull these models directly into your application. This is great, but it can require expensive hardware for large models, and it's only a fraction of what Hugging Face offers in terms of inference.
In this post I will talk about four alternative ways of using models on Hugging Face. Whether the model is small or large, there is always a solution.
1. Dedicated Endpoint
A dedicated endpoint allows you to reserve and rent specific hardware to deploy the selected model on various cloud providers, managed by Hugging Face. This hardware deployment can provide an OpenAI-compatible endpoint for your application if you want. Depending on the size of the model, you may need expensive hardware with GPUs.
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