Google Cloud and NVIDIA expand partnership to scale AI development

Google Cloud and NVIDIA have announced a deepened partnership to enable the machine learning (ML) community with technology that accelerates their efforts to easily build, scale and manage generative AI applications.

To continue bringing AI breakthroughs to its products and developers, Google announced its adoption of the new NVIDIA Grace Blackwell AI computing platform, as well as the NVIDIA DGX Cloud service on Google Cloud. Additionally, the NVIDIA H100-powered DGX™ Cloud platform is now generally available on Google Cloud.

Google also will adopt NVIDIA NIM inference microservices to provide developers with an open, flexible platform to train and deploy using their preferred tools and frameworks. The companies also announced support for JAX on NVIDIA GPUs and Vertex AI instances powered by NVIDIA H100 and L4 Tensor Core GPUs.

“The strength of our long-lasting partnership with NVIDIA begins at the hardware level and extends across our portfolio – from state-of-the-art GPU accelerators, to the software ecosystem, to our managed Vertex AI platform,” said Google Cloud CEO Thomas Kurian. “Together with NVIDIA, our team is committed to providing a highly accessible, open and comprehensive AI platform for ML developers.”

“Enterprises are looking for solutions that empower them to take full advantage of generative AI in weeks and months instead of years,” said Jensen Huang, founder and CEO of NVIDIA. “With expanded infrastructure offerings and new integrations with NVIDIA’s full-stack AI, Google Cloud continues to provide customers with an open, flexible platform to easily scale generative AI applications.” 

Google Cloud has long offered GPU VM instances powered by NVIDIA’s cutting-edge hardware coupled with leading Google innovations. NVIDIA GPUs are a core component of the Google Cloud AI Hypercomputer – a supercomputing architecture that unifies performance-optimized hardware, open software and flexible consumption models. The holistic partnership enables AI researchers, scientists and developers to train, fine-tune and serve the largest and most sophisticated AI models – now with even more of their favorite tools and frameworks jointly optimized and available on Google Cloud. 

“Runway’s text-to-video platform is powered by AI Hypercomputer. At the base, A3 VMs, powered by NVIDIA H100 GPUs gave our training a significant performance boost over A2 VMs, enabling large-scale training and inference for our Gen-2 model. Using GKE to orchestrate our training jobs enables us to scale to thousands of H100 GPUs in a single fabric to meet our customers’ growing demand.”- Anastasis Germanidis, CTO and Co-Founder of Runway.

“By moving to Google Cloud and leveraging AI Hypercomputer architecture with NVIDIA T4 GPUs, G2 VMs powered by NVIDIA L4 GPUs and Triton Inference Server, we saw a significant boost in our model inference performance while lowering our hosting costs 15% using novel techniques enabled by the flexibility that Google Cloud offers.”- Ashwin Kannan, Sr Staff Machine Learning Engineer, Palo Alto Networks.

“Writer’s platform all comes together through this extremely productive partnership with Google and NVIDIA. We’re able to use NVIDIA GPUs optimally for training and inference. We leverage NVIDIA NeMo to build our industrial-strength models, which generate 990,000 words a second with over a trillion API calls per month. We’re delivering the highest quality models that exceed those from companies with larger teams and bigger budgets – and all of that is possible with the Google and NVIDIA partnership. The benefits of their AI expertise are passed down to our enterprise customers, who can build meaningful AI workflows in days not months or years.”- Danny Leung, Director of Alliances, Writer.

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