1.5% of Hugging Face repos take 99.2% of downloads, and the ceiling is Chinese

Hugging Face's Summer 2026 hub review reports public model repositories growing from 2.43 million to 2.96 million over the period, with datasets going from 711,000 to 1 million and Spaces from 1.00 million to 1.44 million ^[1]. In the same post, 1.5% of repositories account for 99.2% of all downloads ^[2], which means the count that gets quoted in vendor decks and the count that matters to anyone picking a model differ by roughly two orders of magnitude.

Run the arithmetic on Hugging Face's own percentages and the shortlist gets concrete. Applied to the 2.96 million model repos, 1.5% is about 44,000 repositories that absorb essentially all traffic ^[1]. The 85.6% of models with fewer than 200 lifetime downloads ^[3] works out to roughly 2.53 million repositories that are, functionally, archived files ^[2].

At the top of the size range the split is geographic. Hugging Face reports that in almost every month of 2026 the largest and most performant open model from a Chinese lab was bigger than anything an American lab released of its own, with China's monthly ceiling running between 754 billion and 2.78 trillion parameters ^[4]. The American ceiling stayed under 130 billion in five of seven months, the exceptions being NVIDIA's Nemotron 3 Ultra at 561 billion in May and June and Inkling from Thinking Machines Lab ^[5]. At the extremes that is about a 21x gap in parameter count ^[3].

The portfolio shapes diverge too. Moonshot, MiniMax, Xiaomi and Z.ai publish almost nothing below 70 billion parameters, so a developer's first encounter is a model too large to run locally ^[6], while Tencent and Alibaba Qwen cover the range from under 1 billion upward ^[7]. Xiaomi and Meituan both cleared a trillion parameters this year ^[8]. The frontier-only strategy rests on a dependency worth naming: Hugging Face argues a lab no longer has to ship a small model to be reachable, because the community quantization layer makes a large one runnable within days ^[9].

American participation has moved desks rather than disappeared. The two organisations publishing the most new open models this year are AMD and NVIDIA, each with more than 200 new model repositories, with LiquidAI third at around 100 ^[10]. Google and Meta now rank well below NVIDIA in new releases ^[11], and Meta has moved toward closed flagship models ^[12]. Above 100 billion parameters, Hugging Face says most U.S. releases this year are not new models but are built on top of Chinese ones ^[13]; the named originals are Inkling at 952B, Nemotron 3 Ultra at 561B, Nemotron 3 Super at 124B, and Arcee AI's Trinity-Large at 399B ^[14]. AMD contributed many conversions and no original model at that scale ^[15]. Chinese open models, meanwhile, are increasingly optimised for domestic chips ^[16].

One more number for anyone using engagement as a proxy for adoption: of the top 25 repos by downloads this year and the top 25 by likes, exactly one appears on both lists ^[17]. No model published in 2026 reaches the download top 25, and thirteen of the twenty-five date from 2022 ^[18]. all-MiniLM-L6-v2 was pulled 1.55 billion times in seven months against 5,156 likes ^[19], about 300,000 downloads per like ^[4], against roughly 60 per like for Kimi-K3 ^[20] - a ratio gap near 5,000x ^[5].

Watch whether any 2026 release breaks into the download top 25, whether AMD publishes an original model above 100B rather than conversions, and whether the quantization layer keeps absorbing trillion-parameter releases fast enough to justify frontier-only portfolios.