State of Open Source on Hugging Face: Spring 2026

This post examines how the open source AI landscape has shifted across competition, geography, technical trends, and emerging communities over the past year. We primarily examine community activity on Hugging Face across many types of metrics to give a holistic view of the ecosystem.

This post builds on an earlier analysis conducted mid-2025, available here , which examined what the Hugging Face Community is building. We recommend reading additional perspectives on the open source ecosystem in and outside of Hugging Face from the Data Provenance Initiative , Interconnects , OpenRouter and a16z , and MIT and the Linux Foundation . As the Hugging Face ecosystem is distributed, analyses are a combination of Hugging Face and community members' work, each of which is appropriately credited.

Activity in the open source AI ecosystem has rapidly grown, with the number of users, model, and dataset repositories all close to doubling. In 2025, Hugging Face grew to 13 million users, more than 2 million public models, and over 500,000 public datasets. This growth signals more than increased interest in open source; it reflects a shift toward active participation, with users increasingly creating derivative artifacts such as fine-tuned models, adapters, benchmarks, and applications rather than only consuming pre-trained systems.

More companies, both large and small, are building on open source. Over 30% of the Fortune 500 now maintain verified accounts on Hugging Face. Startups frequently use open models as default components: Thinking Machines built its Tinker model options entirely on open weights, while popular IDEs such as VSCode and Cursor support both open and closed models. Established American companies such as Airbnb have increased their engagement with the open ecosystem , and Hugging Face has seen more legacy companies upgrading their organizational subscriptions over the course of 2025.

Open source AI is increasingly tied to questions of sovereignty. Open weight models allow governments and public institutions to fine-tune systems on local data under national legal frameworks. Models that can be deployed on domestic hardware reduce reliance on foreign-controlled cloud infrastructure. Transparency around model architecture, training processes, and evaluation supports regulatory review and public accountability. Read more about the open source approach to sovereignty here .

This small-model dominance occurs in part because far more models are released at that size. But even when normalizing for this, the data from the ATOM Project's Relative Adoption Metric shows that the median top-10 models from 1-9B parameters are only downloaded about 4x more than models above 100B. Automated systems and CI pipelines further inflate small model download counts, but the trend toward smaller, deployable models is real.

The mean size of downloaded open models rose from 827M parameters in 2023 to 20.8B in 2025, driven largely by quantization and mixture-of-experts architectures. The median, however, increased only marginally, from 326M to 406M parameters. This divergence indicates that high-end LLM users are pulling up the mean while underlying small-model usage remains stable.

Model development has increasingly emphasized accessibility alongside scale. Smaller models are downloaded and deployed at far higher rates than very large systems, reflecting practical constraints around cost, latency, and hardware availability.

Data from Hugging Face | Hugging Face's two million models and counting: Graph and story by AI World

The ecosystem remains highly concentrated. Approximately half of the models on Hugging Face have less than 200 total downloads, and the top 200 most downloaded models, or 0.01% of models, comprise 49.6% of all downloads.

Specialized communities form around particular domains, languages, or problem areas, and often show sustained engagement and reuse even when their overall download counts are modest. Open source AI is best understood as a collection of overlapping sub-ecosystems rather than a single uniform market.