オープンソースLLMの勢力図2026年夏、中国勢とQwenをデータで整理

"Public model repositories grew from 2.43 to 2.96 million over the period, datasets from 711,000 to 1 million, Spaces from 1.00 to 1.44 million. The distribution underneath stays extreme, roughly 85.6% of models have fewer than 200 lifetime downloads, and 1.5% of repositories account for 99.2% of all downloads. Everything below happens inside that shape."（冒頭）／"In almost every month of 2026, the largest and most performant open model from a Chinese lab was larger than any model an American lab released. China's monthly ceiling ran between 754B and 2.78 trillion parameters; U.S. models 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."（1. The frontier is moving fast）— Hugging Face 公式ブログより

"Moonshot, MiniMax, Xiaomi and Z.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."／"Building large stopped being a differentiator."／"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 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."（いずれも 1. The frontier is moving fast）— Hugging Face 公式ブログより

"The United States is not absent from open source."／"generate hundreds of millions of downloads annually"／"U.S. open source AI is growing."／"some U.S. model releases above 100B parameters this year are built on top of Chinese models or leverage artifacts from Chinese labs"（いずれも 1. The frontier is moving fast）— Hugging Face 公式ブログより

"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."／"a model optimized for your hardware and freely available is the clearest proof that the hardware works."／"More hardware and infrastructure organizations such as NVIDIA are training and open-weighting competitive models."／"AMD contributed many conversions. This work is important: it enables trillion-parameter models to run efficiently on U.S. hardware."（いずれも 1. The frontier is moving fast）— Hugging Face 公式ブログより

"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."／"Not one model published in 2026 reaches the download top 25, while thirteen of the twenty-five date from 2022. all-MiniLM-L6-v2 was pulled 1.55 billion times in seven months against 5,156 likes; Kimi-K3 was pulled about 60 times per like it received."／"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."（いずれも 2. Attention ≠ Adoption）— Hugging Face 公式ブログより

"China's frontier labs are the only accounts on the Hub where the heavy band carries the volume. Effectively all of MiniMax's 2026 downloads are of models above 70B, along with 88% of Moonshot's, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B, and NVIDIA and Meta only 14% and 9%."／"Moonshot’s frontier-only portfolio recorded 37M downloads over the year, while Qwen’s broader release strategy across model sizes reached 2,045M"／"about 55 times more"（いずれも 2. Attention ≠ Adoption）— Hugging Face 公式ブログより

"A model’s ecosystem position is not defined only by its own releases, but by how much the community builds on top of it."／"Qwen-based models now account for 151,448 derivatives on the Hub, 2.6× Meta’s total footprint and 4.7× the Llama repositories specifically. Google follows with 82,506 derivatives."／"Qwen derivatives have increased at roughly 180–210 new repositories per day throughout the first seven months of 2026, showing that adoption is not driven only by individual launches. Qwen has become part of the default workflow for developers deciding what models to fine-tune and deploy."／"Several factors contributed to this position. First, consistency. Qwen has maintained a regular release cadence, continuously updating its model family rather than relying on occasional flagship releases. Second, coverage. It publishes models across a wide range of sizes and use cases, allowing developers to stay within the same ecosystem whether they need a small local model or a larger deployment model. Third, openness. Apache 2.0 licensing reduces friction for modification, redistribution, and commercial use."／"These factors reinforce each other. A broad model family attracts more developers; more developers create more derivatives; and those derivatives make the ecosystem more attractive to future users."／"This position was built largely by the community. The 151,448 derivatives represent downstream work created by other developers, not releases produced by Qwen itself. Even among the 28,531 GGUF conversions of Qwen models on the Hub, Qwen published only 54."（いずれも 4. Qwen has become the community's base model）— Hugging Face 公式ブログより

"The third-largest source is Unsloth, a community account publishing quantized and fine-tuning-ready builds, many of which further extend the Qwen ecosystem."（4. Qwen has become the community's base model）— Hugging Face 公式ブログより

"If frontier models were a licensing business, you would expect the biggest releases to carry the tightest terms. However, the data below shows a different story."／"Of 178 Chinese releases above 20B parameters this year, 59% carry Apache 2.0 and 22% carry MIT, and almost none carry non-commercial restrictions."／"However, in the last few weeks, we started to see a change on this trend for the really large models, with Kimi K3 and Qwen 3.8 2.4T starting to include some non-commercial restrictions and revenue share requirements to their licenses"／"DeepSeek and Z.ai ship models between 700 billion and 1.65 trillion parameters under plain MIT."／"Whatever these releases are for, it is not licence revenue. The weights are given away on the most permissive terms available. The return has to come from somewhere else: API and cloud business, hardware and platform positioning, or the ecosystem position itself."（いずれも 3. Open weights shift where value accumulates）— Hugging Face 公式ブログより

"Chinese labs license their largest models about as permissively as their smallest, and more permissively than American labs license theirs: on the American side of the same size band, 29% is Apache or MIT, 41% sits under custom terms and 30% declares nothing at all."（3. Open weights shift where value accumulates）— Hugging Face 公式ブログより

"Among models that declare a parameter count, those under 1B take 83% of all-time downloads and everything above 100B takes 1%. Restricting to downloads accumulated in 2026 changes nothing: 3% of the volume goes to models above 70B."／"small models are the only ones that run on the hardware most developers actually have."（いずれも 5. Small models remain the practical layer）— Hugging Face 公式ブログより

"In February the ggml team joined Hugging Face, with the project remaining fully open-source, community-governed and in the same technical direction. What changed is that the most important project in local inference now has durable resources behind it."／"The ceiling moved with llama.cpp. The July snapshot carries GGUF builds of DeepSeek-V4-Flash at roughly 284B parameters and Kimi-K3 at roughly 2.8 trillion. Local inference used to mean an 8B model on a laptop. It now means a trillion-parameter mixture-of-experts spread across a few consumer machines, which is the alternative route the frontier did not have a year ago, and the reason a frontier-first release strategy is viable at all."（いずれも 5. Small models remain the practical layer）— Hugging Face 公式ブログより

"And that route runs on Qwen: 39.6 million GGUF downloads a month, nearly twice Gemma's 20.8 million and more than five times Llama's 7.5 million. The Llama gap is not a supply problem, Llama-derived GGUF repositories slightly outnumber Qwen's. Same shelf space, a fifth of the traffic."／"Across the ten largest model families, the labs behind these models publish very few official GGUF conversions. Yet GGUF versions are often the ones used by developers running models locally."／"labs could collaborate with existing ecosystem contributors such as Unsloth."（いずれも 5. Small models remain the practical layer）— Hugging Face 公式ブログより

"We could not have written this section in March, because the instrument did not exist."／"For the first time we can see how much agent traffic the Hub receives and which harnesses it comes from."（いずれも 6. Agents are the new user）— Hugging Face 公式ブログより

"Claude Code led July with 44.4%, but a single month conceals the real finding: it held 67.8% in April and 64% in May, while Codex climbed steadily from 10.4% to 20.8%. This is a market with no incumbent, where one release or one changed default can move half the traffic in a month."（6. Agents are the new user）— Hugging Face 公式ブログより

"The second finding is the unregistered row. Nearly a quarter of agent-tagged traffic in July came from harnesses not yet named in the dataset, and in May that figure was 59.8%. Between April and July more than a dozen new client identifiers appeared. New entrants are arriving faster than any registry can name them — which is itself the finding."（6. Agents are the new user）／"We spent much of the year building for this reader rather than only for human browsers."（Data from Hugging Face）／"In the end, with agents being the number 1 user on HF Hub for the first time, the next report may look very different."（Looking forward）— Hugging Face 公式ブログより

"This analysis is based on activity observed on the Hugging Face Hub during the first seven months of 2026."／"They should not be interpreted as direct measures of model quality, commercial adoption, or overall market share."（Notes on method）— Hugging Face 公式ブログより