NVIDIA and Microsoft are to build one of the most powerful AI supercomputers in the world, powered by Microsoft Azure’s advanced supercomputing infrastructure combined with NVIDIA GPUs, networking and full stack of AI software to help enterprises train, deploy and scale AI, including large, state-of-the-art models.
Azure’s cloud-based AI supercomputer includes powerful and scalable ND- and NC-series virtual machines optimized for AI distributed training and inference. It is the first public cloud to incorporate NVIDIA’s advanced AI stack, adding tens of thousands of NVIDIA A100 and H100 GPUs, NVIDIA Quantum-2 400Gb/s InfiniBand networking and the NVIDIA AI Enterprise software suite to its platform.
As part of the collaboration, NVIDIA will utilize Azure’s scalable virtual machine instances to research and further accelerate advances in generative AI, a rapidly emerging area of AI in which foundational models like Megatron Turing NLG 530B are the basis for unsupervised, self-learning algorithms to create new text, code, digital images, video or audio.
The companies will also collaborate to optimize Microsoft’s DeepSpeed deep learning optimization software. NVIDIA’s full stack of AI workflows and software development kits, optimized for Azure, will be made available to Azure enterprise customers.
“AI technology advances as well as industry adoption are accelerating. The breakthrough of foundation models has triggered a tidal wave of research, fostered new startups and enabled new enterprise applications,” said Manuvir Das, vice president of enterprise computing at NVIDIA.
“Our collaboration with Microsoft will provide researchers and companies with state-of-the-art AI infrastructure and software to capitalize on the transformative power of AI.”
Scalable Peak Performance With NVIDIA Compute and Quantum-2 InfiniBand on Azure
Microsoft Azure’s AI-optimized virtual machine instances are architected with NVIDIA’s most advanced data center GPUs and are the first public cloud instances to incorporate NVIDIA Quantum-2 400Gb/s InfiniBand networking.
Customers can deploy thousands of GPUs in a single cluster to train even the most massive large language models, build the most complex recommender systems at scale, and enable generative AI at scale.
“AI is fuelling the next wave of automation across enterprises and industrial computing, enabling organizations to do more with less as they navigate economic uncertainties,” said Scott Guthrie, executive vice president of the Cloud + AI Group at Microsoft.
“Our collaboration with NVIDIA unlocks the world’s most scalable supercomputer platform, which delivers state-of-the-art AI capabilities for every enterprise on Microsoft Azure.”
The current Azure instances feature NVIDIA Quantum 200Gb/s InfiniBand networking with NVIDIA A100 GPUs. Future ones will be integrated with NVIDIA Quantum-2 400Gb/s InfiniBand networking and NVIDIA H100 GPUs.
Combined with Azure’s advanced compute cloud infrastructure, networking and storage, these AI-optimized offerings will provide scalable peak performance for AI training and deep learning inference workloads of any size.
Accelerating AI Development and Deployment
Additionally, the platform will support a broad range of AI applications and services, including Microsoft DeepSpeed and the NVIDIA AI Enterprise software suite.
Microsoft DeepSpeed will leverage the NVIDIA H100 Transformer Engine to accelerate transformer-based models used for large language models, generative AI and writing computer code, among other applications.
This technology applies 8-bit floating point precision capabilities to DeepSpeed to dramatically accelerate AI calculations for transformers — at twice the throughput of 16-bit operations.
NVIDIA AI Enterprise — the globally adopted software of the NVIDIA AI platform — is certified and supported on Microsoft Azure instances with NVIDIA A100 GPUs. Support for Azure instances with NVIDIA H100 GPUs will be added in a future software release.
NVIDIA AI Enterprise, which includes the NVIDIA Riva for speech AI and NVIDIA Morpheus cybersecurity application frameworks, streamlines each step of the AI workflow, from data processing and AI model training to simulation and large-scale deployment.
