Build faster, train smarter, scale farther with DigitalOcean’s GPU cloud platform
Run large-scale AI models, process complex data faster, and build high-performance computing with ease. DigitalOcean’s GPU cloud platform gives you flexible, cost-efficient access to dedicated GPU instances, with no complex setup and no hidden costs.
Your infrastructure, accelerated with GPU Power
From deep learning to 3D rendering, complex simulations to AI inference, modern workloads need accelerated processing power. That’s where GPUs shine. With DigitalOcean’s GPU cloud platform, you get on-demand access to powerful GPU instances without the overhead of managing physical servers or expensive hardware. Whether you're training custom models, running real-time AI applications, or powering scientific research, our GPU cloud infrastructure gives you the freedom to scale, experiment, and deploy at your own pace. Everything runs in a secure, developer-friendly cloud environment, so you can focus on building breakthroughs, not managing infrastructure.
Build high-performance AI and HPC workflows on DigitalOcean’s GPU cloud platform
Whether you're training next-generation AI models, performing scientific research, or running graphics-intensive workloads, GradientAI GPU Droplets gives you the power and flexibility to build, scale, and deploy faster, without complexity.
Purpose-built GPUs for your use case
Get access to the latest NVIDIA GPUs, like L40S, H100, RTX 4000 Ada, and RTX 6000 Ada, along with AMD options like MI300X and MI325X, optimized for diverse workloads like AI training and large-scale simulations. Select the configuration that fits your project to train, fine-tune LLMs, and deploy your deep learning models at scale.
Boost performance with direct-to-GPU networking
Use direct-to-GPU networking for low-latency data transfer between CPUs and GPUs, minimizing bottlenecks in your pipelines. Speed up workloads without costly or complex network management.
Right-size your GPU resources instantly
Scale up or down on demand with flexible GPU Droplets that grow with your workloads. No proprietary tooling, no vendor lock-in, just GPU power, ready when you need it.
Build GPU clusters with ease
Use simple APIs or Terraform to orchestrate GPU clusters for distributed training or parallel workloads. DigitalOcean makes multi-GPU setups easier to deploy and manage, with clear pricing and intuitive controls.
Clear, fair GPU pricing, always
**Avoid bill shock and the unpredictable billing of hyperscalers like AWS, Google Cloud, and Microsoft Azure. With DigitalOcean’s predictable GPU pricing, you can confidently manage budgets without worrying about hidden fees or surprise costs.
Launch in minutes with ready-to-use environments
Spin up GPU instances pre-installed with CUDA drivers and optimized AI/ ML libraries. Get started quickly with PyTorch, TensorFlow, or any AI framework you prefer.
Learn more about the DigitalOcean GPU cloud platform
The GradientAI GPU Droplets give you the flexibility to run a wide range of AI, ML, and high-performance computing (HPC) workloads, no matter the size of your project. Whether you're training massive AI models, rendering complex graphics, analyzing big data, or running real-time inference, you can choose the compute power that fits your needs today and scale as those needs grow.
Handle complex AI workloads with advanced GPU instances
Run demanding AI/ML tasks like LLM training, deep learning, and fine-tuning models with high-memory GPUs such as NVIDIA H100 and AMD MI300X.
Instances deliver faster throughput and help you avoid memory bottlenecks when handling large-scale data or models.
Render and create high-performance media
Power your creative workflows with GPUs optimized for graphics and media workloads, like the NVIDIA RTX 4000 Ada and 6000 Ada.
DigitalOcean Gradient AI GPUs are ideal for video rendering/3D modeling/visual effects and other compute-intensive creative applications.
Analyze massive datasets faster
Access GPU-accelerated compute for data analytics, simulations, and HPC.
Options like the MI325X or MI300X support large datasets and high-throughput analytics for faster processing of scientific workloads/financial modeling and complex simulations.
Run inference in real time
Deploy real-time AI applications, such as chatbots, recommendation engines, computer vision systems, or virtual assistants, with low-latency inference.
GPUs like L40S and A100 are well-suited for scalable inference pipelines
Serve multiple requests in parallel without infrastructure headaches.
Scale effortlessly as you grow
Start small and scale big with 1 to 8 GPU configurations.
Whether you need a single GPU for experimentation or multi-GPU deployments for production—you can scale vertically or horizontally based on your project requirements with predictable pricing.
Resources to help you build
Get the most out of multi-GPU setups
Learn how to deploy your workloads with multi-GPU computing.
Avoid GPU memory bottlenecks
Discover how GPU memory hierarchy can make or break your compute performance.
How to choose the right cloud GPU
Not sure which GPU to pick? Get practical tips to match your workloads to the right cloud GPU.
FAQs
What is a GPU cloud platform?
A GPU cloud platform provides scalable, on-demand access to graphics processing units (GPUs) via the cloud. Instead of purchasing and maintaining expensive GPU hardware, you can rent powerful GPU instances for tasks like AI model training, deep learning, 3D rendering, and scientific simulations. DigitalOcean offers GPU Droplets that make it easy to deploy NVIDIA and AMD GPUs with a simple setup and transparent pricing.
Who should use a GPU cloud platform?
A GPU cloud platform is ideal for anyone running compute-intensive workloads that require accelerated processing. This includes AI and machine learning developers training large models, researchers running complex simulations, 3D artists and game developers working with graphics and rendering pipelines, and data analysts processing massive datasets. DigitalOcean GPU Droplets are useful for developers, startups, and growing teams that need access to scalable GPU power without managing hardware.
Which GPUs are commonly offered?
Cloud GPU platforms provide a range of GPUs designed for different use cases. High-end options like NVIDIA H100 are commonly used for AI training, HPC, and large-scale inference. For graphics-heavy workloads, GPUs such as the NVIDIA L40S and RTX 6000 Ada are popular. AMD’s MI300X, MI300A, and MI325X GPUs are also offered for memory-intensive AI and HPC applications. DigitalOcean gives you access to these GPUs with flexible configurations, from single-GPU instances to 8-GPU clusters, so you can scale according to your project’s needs.
How to pick a GPU for AI?
Selecting the right GPU for AI depends on your workload requirements. If you’re training large AI models or handling complex deep learning tasks, GPUs with high memory and advanced tensor cores like the NVIDIA H100 or AMD MI300X are recommended. For real-time AI inference, mid-range GPUs such as the NVIDIA L40S provide efficient, scalable performance. If you’re experimenting or running prototypes, GPUs like the NVIDIA RTX 6000 Ada offer a balance of power and cost. DigitalOcean makes this process easy by allowing you to choose from a range of GPU options based on your workload.
Who makes GPUs for AI?
The two leading manufacturers of AI GPUs are NVIDIA and AMD. NVIDIA is widely known for its CUDA ecosystem and products like the H100, A100, L40S, and RTX 6000 Ada, which are optimized for AI training and inference. AMD offers the MI300 series, including MI300X and MI325X, focusing on large memory capacity and open-source software support with ROCm. DigitalOcean provides access to both NVIDIA and AMD GPUs, giving you flexibility to choose the right platform for your AI or HPC projects.
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Get started with DigitalOcean’s GPU cloud platform for AI workloads
