Dell Technologies announces that CoreWeave has purchased thousands of Dell PowerEdge servers, expanding access to compute power organizations need to innovate using AI and generative AI (GenAI).
CoreWeave is a specialized cloud provider for large-scale NVIDIA GPU-accelerated workloads. The company will use Dell PowerEdge XE9860 servers with NVIDIA H100 Tensor Core GPUs as the key infrastructure powering its cloud solutions, tailored for workloads like AI, machine learning (ML), visual effects (VFX) rendering and large-scale simulations.
“AI is driving explosive demand for high performance cloud solutions that can provide the compute that large models need to scale, and we are providing the gold standard of cloud infrastructure to support these compute intensive workloads,” said Brian Venturo, co-founder and chief technology officer, CoreWeave. “Our custom-built, modern cloud infrastructure delivers the best possible performance for every workload, and collaborating with Dell Technologies allows us to do this on an even greater scale.”
CoreWeave is delivering the next generation of cloud computing, with a modern approach to hardware engineering and proprietary software stack that is designed specifically for the most complex and intensive workloads. With this new agreement, CoreWeave customers around the world can now tap into thousands of accelerated Dell servers within seconds to speed compute-intensive workloads.
“AI is a powerful, completely transformational tool for businesses of all sizes but only if you have the right IT foundation in place,” said Jeff Clarke, chief operating officer and vice chairman, Dell Technologies. “By bringing our most powerful Dell PowerEdge servers with NVIDIA H100 Tensor Core GPUs to CoreWeave customers, we’re helping meet demand for advanced compute at scale.”
Built for extreme acceleration of AI, ML and deep learning training, the Dell PowerEdge XE9680 is equipped to deploy AI computing initiatives and help ensure high GPU memory, bandwidth and security. The system can handle the performance demands of large language models of GenAI while delivering it in a dense IT footprint.
SOURCE: PRNewswire