Choosing the Right DigitalOcean Offering for Your AI/ML Workload

At DigitalOcean, we simplify powerful infrastructure to help you focus on building, not managing complexity. Our products are purpose-built for a range of AI and ML workloads, each tailored to different needs and levels of control, ranging from single-tenant GPU servers to our GenAI platform. In this guide, we will explore each option, its key features, and use cases to help you select the ideal solution for your project’s needs. Let’s dive in!

DigitalOcean Bare Metal GPUs

Best for: Scaling businesses who need direct access to physical hardware for high-performance AI/ML workloads.

Bare Metal GPUs are purpose-built to tackle some of the most demanding AI/ML workloads with 8 NVIDIA Hopper GPUs and powerful hardware. These servers are ideal for teams that want to leverage GPUs with full access to system resources, while benefiting from DigitalOcean’s engineering support and enhanced platform reliability. This offering is particularly useful for workloads with strict latency or high resource demands, such as deep learning research and large-scale training. With Bare Metal GPUs, you gain single-tenant, dedicated hardware for maximum performance and privacy—no noisy neighbors—and enjoy cost savings through substantial pricing discounts on longer-term commitments.

Key Features:

• Direct hardware access to maximize performance.

• High-touch engineering support for complex deployments.

• Price-inclusive storage.

When to Choose Bare Metal GPUs: For workloads that require dedicated hardware resources and if you would like to leverage DigitalOcean’s hands-on support. It’s a strong choice for organizations handling high-stakes applications or more stringent performance requirements.

Resources: Bare Metal GPU Product Documentation

DigitalOcean GPU Droplets

Best for: AI/ML engineers, startups, and research institutions focused on training or fine-tuning LLM models, with the flexibility to scale up or down.

GPU Droplets offer the power of NVIDIA H100 Tensor Core GPUs with a virtualization layer, allowing for scalable, on-demand deployment. This flexibility makes GPU Droplets an excellent choice for workloads that fluctuate in intensity or for teams who need fast, easy access to GPUs without managing physical infrastructure. With virtualized instances, users can choose only the resources they need, paying per GPU-hour.

Key Features:

• Scalable, on-demand GPU compute.

• Virtual instances for greater cost efficiency.

• Seamless integration with the broader DigitalOcean ecosystem.

When to Choose GPU Droplets: For workloads that vary in scale, or for users looking for a flexible, more cost-effective entry point into GPU computing, GPU Droplets provide an accessible yet powerful option.

Resources: Scaling GenAI with GPU Droplets and DigitalOcean Networking, Learn How to Build a RAG Application using GPU Droplets, and Watch: Automatic Speech Recognition using NVIDIA NIM on GPU Droplets

DigitalOcean 1-Click Models powered by Hugging Face

Best for: Developers and teams seeking an easy, one-click solution for running generative AI models without infrastructure setup.

1-Click Models powered by Hugging Face on GPU Droplets are designed for fast, one-click deployment of popular third party generative AI models, providing a low-barrier way to get started with AI. Powered by Hugging Face, these 1-Click Models offer quick, turnkey access to powerful third party models, optimized for NVIDIA GPUs. This solution reduces setup time and complexity, making it ideal for developers looking to test or deploy models quickly without the hassle of managing GPU infrastructure.

Key Features:

• Preconfigured with popular third party models.

• 1-click deployment and simplified setup.

• Optimized performance on NVIDIA GPUs.

When to Choose 1-Click Models on GPU Droplets: If you’re looking to run a GenAI model fast and with minimal setup, this solution offers immediate value and high performance for inference tasks, all without needing in-depth ML expertise.

Resources: Getting Started with 1-Click Models on GPU Droplets - A Guide to Llama 3.1 with Hugging Face, Turning Your 1-Click Model GPU Droplets Into A Personal Assistant, and Watch: 1-Click Models Powered by Hugging Face on DigitalOcean

GPUs for DOKS (DigitalOcean Kubernetes Service)

Best for: DOKS users, Kubernetes-savvy teams, and AI/ML startups needing a managed Kubernetes cluster with GPU support.

GPUs for DOKS combines the power of DigitalOcean’s Kubernetes service with NVIDIA H100 Tensor Core GPUs, offering a fully managed solution for teams that want to run scalable AI/ML workloads. With GPU DOKS, users can spin up GPU-accelerated Kubernetes clusters and enjoy native Kubernetes features like autoscaling and workload orchestration. This is a great fit for teams that are familiar with Kubernetes and need AI/ML infrastructure that can grow with their projects.

Key Features:

• Fully managed Kubernetes environment with GPU support.

• Autoscaling and flexible workload orchestration.

When to Choose GPU DOKS: If you’re running containerized workloads and need the flexibility and resilience of Kubernetes, GPU DOKS offers a powerful platform to run and scale ML models. It’s particularly suited to organizations already using Kubernetes who want to add GPU support without the hassle of infrastructure management.

Resources: GPU Worker Node Product Documentation

DigitalOcean GenAI Platform

Best for: Developers building generative AI applications like chatbots or search tools, with simple deployment and agent customization.

Currently in Early Availability, the GenAI Platform is a quick way to get started with third party generative AI models, designed for developers who want to build and deploy GenAI-powered agents in minutes. With features like Retrieval-Augmented Generation (RAG), agent guardrails, and function calling, the GenAI Platform enables users to create powerful AI agents with minimal setup. This platform is especially valuable for businesses interested in incorporating GenAI into their applications without needing deep ML expertise.

Key Features:

• Agent creation with third party foundational models and proprietary knowledge bases.

• Guardrails that enable safer deployment in production.

• Integrated function calling for enhanced interactions.

When to Choose GenAI Platform: If you’re new to AI/ML and need an intuitive, agent-based GenAI solution, this platform allows for fast development and straightforward customization, making it ideal for building AI-driven applications, including chatbots.

Resources: Adding a Chatbot to Your WordPress Website Using DigitalOcean GenAI, Adding a Chatbot to Your Ghost Website Using DigitalOcean GenAI, AI Agents vs AI Chatbots, and Watch: GenAI Platform Function Routing

Choosing the Best Solution for Your Needs

Each of DigitalOcean’s GPU offerings, powered by NVIDIA accelerated computing platform, brings unique strengths suited to different stages and types of AI/ML projects:

• Bare Metal GPUs: For maximum control and performance on dedicated hardware.

• GPU Droplets: For scalable, on-demand GPU compute with a flexible, cost-efficient pricing model.

• 1-Click Models: For one-click access to generative AI models powered by Hugging Face, optimized for ease of use and fast deployment.

• GPUs for DOKS: For teams running containerized workloads who need a managed Kubernetes cluster with GPU support.

• GenAI Platform: For developers looking to quickly deploy generative AI agents with built-in functions and customization options.

By selecting the right option for you, you can leverage DigitalOcean’s strengths in scalability, simplicity, and approachability to power your AI/ML workloads from development to production. Whether you’re training large models, running inference, or creating AI-driven applications, DigitalOcean has the infrastructure to help you build, deploy, and scale with ease.

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