Agile Robots raises $500M in round valuing it at $8.5B

Agile Robots raises $500M in round valuing it at $8.5B

Live•Nov 14, 12:30 AM•2 min read•
Marco Ferrari
Marco Ferrari

Agile Robots was valued at $8.5 billion after raising $500 million in a round led by SoftBank, first reported by Techxplore. The company said the money will fund expansion in the United States, more production capacity in Germany and entry into new markets for robots that automate and speed up factory work.

SoftBank, a Japanese tech investment group, led the round; Nvidia and the Japanese group Hitachi also participated. The valuation was also given as 7.7 billion euros.

Founded in 2018 and based in Munich, Agile makes humanoid robots and robotic arms used to automate and speed factory work. The company says it has deployed around 20,000 robots in automotive, consumer electronics and logistics.

Earlier this year, Agile opened a factory outside Munich that it says can produce up to 10,000 humanoid robots annually to meet rising demand. The stated expansion plan pairs entry into new markets with additional production capacity in Germany.

A company spokesman told AFP Agile was Europe's most valuable physical AI robotics firm. CEO and founder Zhaopeng Chen described the company's vision as robots working alongside people in factories and warehouses.

Agile is among German startups seeking to move AI from digital applications into physical work, particularly at traditional manufacturers. Europe has lagged the United States and China in developing the best-known AI models, though some believe industrial AI deployment could give the continent an edge.

Robotics remains a competitive field: larger Chinese rivals such as Unitree have enjoyed substantial government support, and Unitree is known for its backflipping robots.

Six Chinese AI models carry Blackwell initialization fingerprints

Six models from DeepSeek, Moonshot AI, MiniMax and Ant Group carry weight patterns matching NVIDIA Blackwell GPU configurations, first reported by Unemploymentlabs. The evidence points to Blackwell-compatible initialization for those models, but it does not establish where the chips were located, how access was obtained or whether export controls were violated.

The six models are DeepSeek-V4-Flash and V4-Pro, Moonshot AI’s Kimi K3 and K3.1-Pro, MiniMax-M3, and Ant Group’s Ling-3.1-flash. V4-Flash and MiniMax-M3 match the 148-SM B200/B300 layout; the other four match the 152-SM GB200/GB300 layout.

The fingerprints come from initialization patterns preserved in publicly released weights. Recovering the seed and GPU thread layout identifies the hardware that generated starting values, not necessarily every accelerator used later.

Earlier Hopper fingerprints appear in DeepSeek-V3, Kimi K2.5, MiniMax-M2.7 and Ling-3.0-flash. Kimi K2.7-Code is an intermediate case: its Hopper-era weights include a small part matching B200/B300. Blackwell-matching releases include DeepSeek-V4 in April 2026, MiniMax-M3 in June, Kimi K3 in July, Ling-3.1-flash in October and Kimi K3.1-Pro in November.

The release sequence shows a shift in initialization fingerprints across four labs, not proof that entire training runs used Blackwell.

The strongest evidence for the six models ranged from 49 to 1,385 sigma, while the weakest individual test reached 20 sigma. A specially configured AMD MI300X can reproduce the layouts; for V4-Flash, 363 of 377 values that differed between AMD and NVIDIA matched NVIDIA, and none matched AMD. The same test could not settle the other five models, though an AMD explanation is considered very unlikely.

Run estimates for five models put each at roughly 500 to 3,000 Blackwell GPUs over two to three months, assuming the hardware that initialized a model also trained it. At roughly $70,000 per GPU, the largest cluster would cost $150 million to $200 million, and the five final runs together more than half a billion dollars; total lab spending on export-controlled GPUs is likely in the billions.

A U.S. license is required to export these Blackwell chips to China, and none has been reported.

The newest readable models from Alibaba (Qwen), Zhipu (GLM), Tencent, Xiaomi and StepFun still matched Hopper. Meituan says LongCat-2.0 was trained entirely on AI ASIC superpods, and its released weights showed no NVIDIA fingerprint.