H100 GPU-enabled worker nodes are now available for DigitalOcean Kubernetes (DOKS)

We’re thrilled to announce this powerful new GPU offering for DigitalOcean Kubernetes is now generally available. With this announcement, we’re lowering the barrier to entry for AI innovation by providing flexible, affordable access to high-performance GPU resources. Whether you’re a startup or an enterprise, DigitalOcean delivers the infrastructure and tools necessary for the entire cloud and AI development lifecycle, empowering businesses of all sizes to harness AI and machine learning within Kubernetes environments.

With the official launch, customers can now seamlessly integrate single-GPU or 8 GPU configurations into their Kubernetes clusters, enabling them to power even the most demanding AI/ML workloads.

The Challenge of AI/ML in Containerized Environments

In today’s AI-driven world, organizations are increasingly relying on data to gain actionable insights. However, the complexity and resource demands of AI/ML model development, particularly within containerized environments, pose significant challenges. Many businesses struggle to fully leverage their data due to the high costs and technical barriers associated with AI/ML.

At DigitalOcean, we understand these challenges and are committed to providing solutions that simplify and accelerate innovation. Our GPU-enabled worker nodes for DOKS are designed to remove these barriers, offering a scalable, accessible, and cost-effective solution for businesses eager to harness the power of AI/ML within their Kubernetes clusters.

Use Cases: Where GPUs for Kubernetes Excels

While the NVIDIA H100 GPUs are fast and powerful, they’re particularly well suited to some specific use cases that require massive acceleration for specialized tasks like AI/ML, big data analytics, genomic sequencing, and more. Let’s take a look at some of the specific use cases that benefit from GPU technology.

One of the key differences that GPUs offer is the ability to handle parallel processing, making them the preferred option for training AI models. Some of the best use cases include AI/ML model training and inference, where large datasets are processed and complex neural networks require the speed and efficiency that GPUs provide. Additionally, video processing and rendering workloads, such as transcoding and real-time video streaming, benefit from GPUs’ ability to handle compute-intensive tasks.

GPUs also excel in high-performance batch processing, where workloads like big data analytics and simulations can leverage parallel processing to drastically reduce runtime. Scientific simulations—like molecular dynamics, climate modeling, or genomics—thrive in Kubernetes environments with GPU acceleration, enabling faster analysis and insights. Moreover, financial services firms use GPUs for tasks such as high-frequency trading and risk modeling, where speed and accuracy are critical. With Kubernetes’ ability to orchestrate and scale workloads, GPUs make these tasks more efficient and accessible across distributed systems, ensuring responsiveness even as demand grows.

What You Can Achieve with GPU-Enabled Worker Nodes

Our new GPU offering is tailored to meet the demands of modern AI/ML workloads, providing the flexibility and power you need to:

• AI/ML Experimentation and Development: Accelerate your AI/ML experiments and development within containerized environments, enabling faster iteration and innovation.
• Running Distributed AI Workloads: Efficiently distribute and run complex AI workloads across your Kubernetes clusters, helping to ensure optimal performance and resource utilization.
• Scaling AI Inference Services: Seamlessly scale your AI inference services to meet growing demands, helping to ensure your applications remain responsive and effective.

“Our work at Amorphous Data involves creating personalized AIs built on open source foundation models. This requires substantial computational power, flexible infrastructure, and low management overhead. DigitalOcean’s Kubernetes with H100 GPU nodes provides exactly that. The seamless integration of high-performance GPUs with a robust Kubernetes environment has allowed us to optimize our workflow from data preparation to model training to inference, all within a single cluster.”

— Amorphous Data

Enhanced Features: Bringing AI/ML to Your Kubernetes Clusters

With the integration of NVIDIA’s H100 GPUs, our GPU-enabled worker nodes offer a range of enhanced features to support AI/ML training and inference:

• Kubernetes Integration: Seamlessly add GPU-powered worker nodes to your existing DOKS clusters, allowing you to leverage the full power of Kubernetes for your AI/ML projects.
• Flexible Configurations: Choose from one or eight GPU configurations to match your specific workload needs, helping to ensure you have the right amount of power for every task.
• Scalability: Easily scale your GPU resources within your Kubernetes environment as your AI/ML workloads grow, allowing you to keep pace with your data and business needs.
• Cost-Effectiveness: Our competitive pricing makes AI/ML development more affordable, enabling businesses of all sizes to access the tools they need to succeed in an AI-driven world.

Explore the Full Power of GPU-Enabled Worker Nodes

With the general availability of GPU-enabled worker nodes on DigitalOcean Kubernetes, businesses can now fully unlock the potential of AI/ML development and deployment within a simplified and cost-effective infrastructure. Whether you’re looking to enhance your AI/ML capabilities or scale your existing projects, our GPU offering is here to help you achieve your goals.

Explore how DigitalOcean Kubernetes (DOKS) can help you scale workloads, optimize performance with a developer-friendly approach, and automate infrastructure and software delivery.

If you need any assistance migrating from AWS EKS or other Kubernetes solutions, please get in touch with our Sales team.