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Speed up machine learning, scientific computing, and generative AI with high-performance GPUs on Google Cloud.

Key benefits:

• Run workloads (generative AI, 3D visualization, HPC) with advanced AI hardware/software
• Access diverse GPUs for varied performance and pricing
• Optimize workloads with flexible pricing and machine customizations

Key features

• Diverse GPU Offerings: Compute Engine offers NVIDIA GPUs: RTX PRO 6000, GB300, GB200, B200, H200, H100, L4, P100, P4, T4, V100, A100. Options cover various cost/performance needs.
• Adaptable Performance: Achieve optimal balance of processor, memory, high-performance disk, and up to 8 GPUs per instance. Benefit from per-second billing.
• Use Google Cloud Advantages: Run GPU workloads on Google Cloud and access industry-leading storage, networking, and data analytics.

Google Kubernetes Engine (GKE)

Use GKE scalability, NVIDIA Multi-Instance GPU (MIG) support, and GPU time-sharing for efficient generative AI training, inference, and other compute-intensive workloads. Optimize resource utilization and minimize operational costs.

Vertex AI

Combine NVIDIA accelerated computing with Vertex AI, a unified MLOps platform. Utilize NVIDIA GPUs and AI software (such as, Triton™ Inference Server) within Vertex AI Training, Prediction, Pipelines, and Notebooks to accelerate generative AI development and deployment without infrastructure complexities.

Cloud Run

Deploy generative AI faster with NVIDIA NIM on Cloud Run, a fully managed serverless platform. Cloud Run GPU support speeds up NIM to optimize performance and accelerate gen AI model deployment in a serverless environment.

Dynamic Workload Scheduler

Access NVIDIA GPU capacity on Google Cloud for short-duration AI workloads (training, fine-tuning, experimentation). Flexible scheduling and atomic provisioning enhance resource utilization and optimize costs across services like GKE, Vertex AI, and Batch.

Google Distributed Cloud

The NVIDIA Blackwell platform on Google Distributed Cloud enables secure, on-premises deployment of advanced agentic AI (including Google Gemini models). This offers improved AI performance and scalability for sensitive, regulated workloads, ensuring data privacy, sovereignty, and compliance.

Google Cloud basics

• GPUs on Compute Engine: Compute Engine provides GPUs that you can add to your virtual machine instances. Learn more
• Using GPUs for training models in the cloud: Run the training process for many deep learning models, like image classification, video analysis, and natural language processing. Learn more
• Attaching GPUs to Dataproc clusters: Attach GPUs to the master and worker Compute Engine nodes in a Dataproc cluster to accelerate specific workloads, such as machine learning and data processing. Learn more
• Using GPUs with Dataflow: Using GPUs in Dataflow jobs lets you speed up some of your machine learning and other compute intensive data processing tasks. Learn more

Tutorials

• Learn how to add or remove GPUs from a Compute Engine VM. Learn more
• Installing GPUs drivers: This guide shows ways to install NVIDIA proprietary drivers you’ve created an instance with one or more GPUs. Learn more
• GPUs on Google Kubernetes Engine: Learn how to use GPU hardware accelerators in your Google Kubernetes Engine clusters nodes. Learn more

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