Analysis of the top 50 most downloaded models on Hugging Face
🟢 Which organizations and types of models define the open model ecosystem?
The top 50 represent only 3.4% of all models on Hugging Face, but they account for more than 80% of 45 billion downloads.
The vast majority of activity is concentrated around a small group of leaders, these models shape the face of all open-source AI.
📉 Size matters (and the smaller, the better):
- 92.5% of downloads are models < 1B parameters
- 86.3% — < 500M
- 70% — < 200M
- 40% — < 100M
Clear conclusions: in open-source, small and lightweight models that are suitable for local deployment and edge inference win.
🧠 Popular directions:
- NLP — 58.1%
- Computer Vision — 21.2%
- Audio — 15.1%
- Multimodal — 3.3%
- Time Series — 1.7%
Who creates the most downloaded models?
- Companies - 63.2% (Google leads)
- Universities - 20.7%
- Individual authors - 12.1%
- NGOs - 3.8%
- Other labs - 0.3%
Which types of models win:
- Text encoders - 45% of all downloads
- Decoders - only 9.5%
- Encoder-decoders - 3%
Despite the hype around LLMs, it is not the giants but the utility models that are massively downloaded for integration into proprietary products.
The USA dominates in all categories:
- appears 18 times among the top 50 downloads
- accounts for 56.4% of all downloads
Open-source AI thrives not because of giant LLMs but thanks to compact, fast and practical models that actually work in products and projects.
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