“The researchers based s1 on Qwen2.5, an open-source model from Alibaba Cloud. They initially started with a pool of 59,000 questions to train the model on, but found that the larger data set didn’t offer ‘substantial gains’ over a whittled-down set of just 1,000. The researchers say they trained the model on just 16 Nvidia H100 GPUs.” —

AI researchers at Stanford and the University of Washington were able to train an AI “reasoning” model for under $50 in cloud compute credits, according to a new research paper released last Friday. The model,

A small team of AI researchers from Stanford University and the University of Washington has found a way to train an AI reasoning model for a fraction of the price paid by big corporations that produce widely known products such as ChatGPT.

DeepSeek built a competing large language model (LLM) to OpenAI's ChatGPT, but claims that it trained the model with old Nvidia processors that aren't widely used anymore. This news has shellshocked technologists and analysts across Wall Street because if DeepSeek's methods of building highly capable AI can be done using legacy hardware,