Nvidia’s funding seen as key for US AI firms to compete with China

Building a frontier AI model in America is expensive. Training a model like Reflection AI’s Beam is estimated to cost around $100 million, a price tag that may make it harder for US labs to close the gap with China. That is where Nvidia comes in. The chipmaker has turned itself into something like the sector’s venture fund, landlord and hardware store at once. A $100 million model and a $6 billion bet Reflection AI unveiled Beam on October 5, 2026. It is a sparse model with 501 billion parameters, of which only 23 billion are active at any given moment. Beam was trained on 23.8 trillion tokens using reinforcement learning techniques. The run used 10,500 Nvidia GB300 GPUs over four weeks. Beam is touted as delivering 3-4x better inference efficiency than China’s GLM 5.2. It also claims more than 4x the efficiency of leading Western open models. Inference is the work a model does after training, when it actually answers prompts. Better inference efficiency means each answer costs less to serve. The model is positioned as a cost-effective Western alternative, though it still shows some raw performance gaps against certain Chinese counterparts. In August 2026, Nvidia struck a $6 billio...