The emergence of models like AlphaFold2 has skyrocketed the demand for faster inference and training of molecular AI models. The need for speed comes with unique computational challenges, including algorithmic complexity, memory efficiency, and strict accuracy requirements. To address this, NVIDIA collaborated with partners to provide accelerated solutions like faster equivariant operations and…
]]>The NVIDIA CUDA-X math libraries empower developers to build accelerated applications for AI, scientific computing, data processing, and more. Two of the most important applications of CUDA-X libraries are training and inference LLMs, whether for use in everyday consumer applications or highly specialized scientific domains like drug discovery. Multiple CUDA-X libraries are indispensable…
]]>In the wake of ever-growing power demands, power systems optimization (PSO) of power grids is crucial for ensuring efficient resource management, sustainability, and energy security. The Eastern Interconnection, a major North American power grid, consists of approximately 70K nodes (Figure 1). Aside from sheer size, optimizing such a grid is complicated by uncertainties such as catastrophic…
]]>AI models for science are often trained to make predictions about the workings of nature, such as predicting the structure of a biomolecule or the properties of a new solid that can become the next battery material. These tasks require high precision and accuracy. What makes AI for science even more challenging is that highly accurate and precise scientific data is often scarce…
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