The world is entering a new era of AI, powered by foundation models and state-of-the-art deep learning. These new models are enabling organizations to achieve new and exciting discoveries across numerous areas of research such as medical image analysis, large language models for science, and multi-physics simulation workloads. While the potential for using foundation models and deep learning to accelerate these discoveries is immense, these state-of-the-art models come with their own training and management challenges.
SambaNova’s Foundation Model Platform delivers value and innovation across the full AI stack – hardware, software, systems, and even pre-trained models – to enable research organizations to achieve a performance advantage over GPU based systems on the most challenging foundation models and deep learning workloads. For research organizations, this means more experiments and more discoveries with the potential to change the world.
Large language models (LLMs) can unlock insights in unstructured data with human level accuracy and solve dozens of language tasks with a single model. Beyond traditional language tasks, these models have demonstrated potential in scientific domains by becoming ‘experts’ in specific topics, such as genomic data for Covid-19 research.
Surrogate models are deep learning models that replace one or more components of larger multi-physics simulation workloads, such as computational fluid dynamics or weather forecasting. However, the GPU/CPU-based architectures used to compute these simulation workloads struggle to deliver performance on sparse, detailed deep learning models.