Key takeaways
- In the AI world, time feels compressed.
- In almost every month of 2026, the largest and most performant open model from a Chinese lab was larger than anything an American lab…
- Xiaomi and Meituan both cleared a trillion parameters this year, and neither was a household name in open weights twelve months ago.
What happened
In the AI world, time feels compressed. A few months after our spring report in our biannual analysis worked through the ecosystem, there are quite a few findings that we have observed until this summer. This report lays out these observations from January to August 2026 and presents the data behind each one. Models and datasets on HF hub are growing on a daily basis. 44 million.
Google and Meta now rank well below NVIDIA in new model releases, despite being the companies that defined open model publishing in previous years. Meta’s move toward closed flagship models further highlights this change. Open source has moved from model labs to hardware and infrastructure companies. At the frontier scale, the picture is very different. S.
releases above 100B parameters this year are not new models, but built on top of Chinese models. Only a few major original American models appear at this scale: Thinking Machines’ Inkling (952B), NVIDIA’s Nemotron 3 Ultra (561B), Nemotron 3 Super (124B), and Arcee AI’s Trinity-Large (399B). AMD contributed many conversions but no original model at this scale.
This work is still important: it enables trillion-parameter Chinese models to run efficiently on American hardware. But it represents a distribution and optimization layer rather than model creation. Meanwhile, Chinese open models are increasingly optimized for domestic chips, the same competition in reverse, where models are designed around specific hardware ecosystems. We took the top 25 model repositories by downloads accumulated this year and the top 25 by likes.
Exactly one repository appears in both lists. We counted downloads inside the window rather than lifetime, so nothing is credited for merely having existed longer, and controlling for age makes the split sharper. Not one model published in 2026 reaches the download top 25, while thirteen of the twenty-five date from 2022.
Why it matters
2% of all downloads. Everything below happens inside that shape. 1. The frontier is moving fast There used to be a clear progression path: labs would start by releasing smaller models and gradually work their way toward the top end of the scale. In 2026, several Chinese labs skipped this progression entirely.
In almost every month of 2026, the largest and most performant open model from a Chinese lab was larger than anything an American lab released of its own. 78 trillion parameters; America's own ceiling stayed under 130B in five of seven months, the exception being NVIDIA's Nemotron 3 Ultra at 561B in May and June, and Inkling from Thinking Machines Lab. The chart splits the labs into two camps.
ai publish almost nothing below 70B, so a developer's first encounter with them is a model too large to run on anything they own. Tencent and Alibaba Qwen cover the whole range instead, from under 1B upward. Two things made the first camp possible. Building large stopped being a differentiator.
Xiaomi and Meituan both cleared a trillion parameters this year, and neither was a household name in open weights twelve months ago. And a lab no longer has to ship a small model to be reachable, because the community's quantization layer will make a large one runnable within days, a dependency we return to below. That leaves the size profile as a statement of intent rather than of capability.
A frontier only portfolio stakes everything on benchmark position and API demand. A full spectrum portfolio is a bid to be the family developers standardise on. Both are rational, they are playing for different prizes. The United States, meanwhile, is not absent from open source. The two organizations publishing the most new open models this year are also the companies making the hardware: AMD and NVIDIA.
Each released more than 200 new model repositories, far ahead of the rest of the field, with LiquidAI ranking third at around 100. Hardware vendors have realized that open models are a way to sell chips: a model optimized for your hardware and freely available is the clearest proof that the hardware works. S. participation in open source AI is still growing. However, the center of gravity has shifted.
What to watch
55 billion times in seven months against 5,156 likes; Kimi-K3 was pulled about 60 times per like it received. The two numbers record different acts. A like says a release matters, and goes to frontier models in the weeks after they ship. A download says something is wired into a pipeline that runs on a schedule, and accrues to small, stable models over years.
Likes are the right instrument for reading what the field is excited about, downloads for reading what it currently depends on. Treating either as a proxy for the other is the most common mistake we see in coverage of the Hub, including our own earlier work. The same split appears at the level of the publisher. Chinese frontier labs are the only accounts on the Hub where the heavy band carries the volume.