Next-generation processing power with DigitalOcean
Are you deploying machine learning models and scaling inference workloads? DigitalOcean keeps your AI applications running smoothly without performance bottlenecks or expensive GPU bills.
Scale, manage, and run AI with ease on GPU clusters
What are GPU clusters?
Graphics processing unit (GPU) clusters, or multi-node GPUs, are connected computing nodes outfitted with both traditional CPUs and GPUs to increase overall performance and available computing power. These GPUs are connected and work in tandem to complete calculations and process data simultaneously, also known as parallel computing.
With GPU clusters, you can distribute workloads across servers and run multiple simultaneous workloads, which works well for use cases like deep learning, machine learning, and AI model training and development. These clusters integrate with vector databases, RAG pipelines, file storage, and inference frameworks to help build production-level AI systems.
GPU clusters vs. single-node for AI and machine learning training
Not sure if your workloads would benefit from either a single-node GPU or a GPU cluster? The main factors include the size of your application, scalability requirements, and how much processing speed you need.
A multi-node system (such as a GPU cluster) provides distributed processing, redundancy, horizontal scalability, optimized performance for resource-intensive processes, and support for more dynamic workloads. It’s ideal for cloud services, AI model training, and data-intensive applications.
A single-node GPU offers an easier deployment process, is a less complex environment overall, and can be more cost-effective. However, it has less scalability, can be prone to performance bottlenecks and degradation, and is less redundant (meaning if one component fails, the entire system can go down). It’s ideal for localized AI applications, tasks with limited processing requirements, or testing and development.
For GPU cluster management, available APIs and CLIs include Kubernetes’ API and CLI, Cluster API and Cluster Autoscaler, GPUStack, and several hyperscaler-based offerings.
GPU cluster use cases
GPU clusters are mainly used for high-performance, compute-intensive workloads. With their increased processing power and memory, you can rely on them for:
Artificial intelligence
GPU clusters can parallelize AI tasks, reduce memory processing bottlenecks, and increase overall throughput performance. That includes data loading and augmentation, automating search across datasets, loss calculation, monitoring training status, training AI models, and computing predictions from inputs.
Big data
Data preprocessing, exploratory analysis, real-time data processing, query acceleration, as well as extraction, transformation, and load processes at scale were traditionally CPU heavy, pushing hardware processing limits. GPU clusters can accelerate overall task times for these demanding operations.
LLM fine-tuning
Using GPU clusters for LLM fine-tuning and training optimizes overall operation, speeds up task completion time, and makes it easier to scale operations. Key processes like model partitioning across GPUs, data tokenizing and formatting, saving model states for reference, running validation batches, model scaling, larger dataset integration, and sharing gradients across GPUs all benefit from this approach.
Inference
GPU clusters help inference applications parallelize tasks, which results in smoother operations and high-capacity model support. This covers model loading, input preprocessing, request batching and queueing, latency optimization, running the model to get predictions, and autoscaling and load balancing.
GPU clusters and open source ML frameworks
Using open-source machine learning (ML) frameworks—such as TensorFlow, PyTorch, Hugging Face Transformers, and RAPIDS—offer GPU support to accelerate training and inference, especially for large datasets, complex models, and high-throughput applications.
Here’s a look at how these frameworks provide AI workload support:
| Framework | GPU Support | Use Case Examples | Cluster Integration |
|---|---|---|---|
| PyTorch | Native CUDA support; DistributedDataParallel for multi-GPU | Computer vision, NLP, LLMs | PyTorch Lightning, TorchElastic, Ray |
| TensorFlow | GPU via tf.distribute.MirroredStrategy, XLA compiler | Deep learning, image/video models | TFJob (KubeFlow), Horovod, GKE/AKS |
| Hugging Face Transformers | Built on PyTorch/TF, optimized for GPUs with accelerate, optimum, vLLM | LLMs, BERT, summarization | DeepSpeed, Transformers + Ray/K8s |
| RAPIDS (cuDF, cuML) | GPU-native dataframes and ML pipelines | Big data + ML workflows | Dask + RAPIDS on Kubernetes |
| Horovod | Distributed training for TensorFlow, PyTorch, MXNet | Synchronized multi-GPU training | MPI or Kubernetes-based |
| DeepSpeed | Optimizes memory and speed for LLM training/inference | GPT, OPT, LLaMA models | Scales to hundreds of GPUs |
DigitalOcean GPU Droplets: Powerful, scalable infrastructure for AI and ML
Scalable
Set up GPU Droplets with just a few clicks and immediately start running your workloads on DigitalOcean. You can access configurations as small as single GPUs or as large as 8 GPUs and scale as needed with this virtualized hardware offering. Vertex, SageMaker, and Azure AI offer similar GPU clusters, but often at increased complexity and expense for digital native enterprises.
Cost effective
With on-demand GPU setups, you can save up to 75% of your costs compared to major hyperscalers* and benefit from our transparent pricing policies.
*Up to 75% cheaper than AWS for on-demand H100s and H200s with 8 GPUs each. As of April 2025.
Interoperable
Built with open source standards, our GPU Droplets are compatible with projects to support open source OSes, log management, storage, and containers. Our GPU Droplets also come pre-installed with Python and Deep Learning software packages and support PyTorch and CUDA frameworks.
Reliabe
All GPU Droplets are HIPAA-eligible and SOC 2 compliant and supported by enterprise-grade SLAs to keep all of your workloads running and online.
Learn more about DigitalOcean Infrastructure
DigitalOcean provides a wide range of hardware to use with our GPU Droplets so you can support AI workloads at scale. You can choose from NVIDIA and AMD GPUs to best configure your infrastructure and train, maintain, and deploy AI with ease. Benchmarks are available at nvidia.com and amd.com.
NVIDIA H100
Suited for LLM training, high-performance computing, and training large language models.
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Up to 4X faster training over NVIDIA A100 for GPT-3 (175B) models.
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Uses the NVIDIA Hopper architecture to increase large language model speed by 30X.
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Can also support computer vision, speech AI, RAG, and conversational AI applications.
NVIDIA RTX 4000 Ada Generation
Ideal for inference, media, 3D modeling, graphical processing, and content creation.
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Supports CUDA 12.2, OpenCL 3.0, and DirectCompute APIs
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Has 1.5X the speed of the previous generation for single-precision floating-point (FP32) operations, making it ideal for large datasets and AI-intensive workloads.
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Up to 1.7X higher performance than NVIDIA RTX A4000.
NVIDIA RTX 6000 Ada Generation
Built for inference, graphical processing, virtual workstations, and computing.
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Delivers 2X higher inference performance compared to the previous generation.
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Runs 568 4th-generation Tensor Cores and 18,176 CUDA cores to increase performance for AI, graphics, and rendering workloads.
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Up to 10X higher performance than NVIDIA RTX A6000.
NVIDIA L40S
Suited for generative AI, inference and training, rendering, virtual workstations, and 3D content.
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Cost-efficient for inference, digital twins, and graphics.
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Provides 5X higher inference performance than the previous generation NVIDIA A40.
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Has 48GB of GDDR6 memory with error-correcting code to support multimodal generative AI workloads.
AMD Instinct MI300X
Designed for large model training, fine-tuning, inference, and high-performance computing.
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Has a high memory bandwidth and capacity for larger models and datasets.
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Designed with 204 high-throughput compute units and 192GB of HBM3 memory on a GPU accelerator.
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Up to 1.3X the performance of AMD MI250X for AI use cases.
Resources to get started with GPU computing power
GPU clusters FAQ
What are GPU clusters used for?
GPU clusters can be used for workloads that require large-scale parallel processing power, such as big data, high-performance computing, and AI. More specific AI use cases that you would use GPU clusters for include model training, inference, and pre-trained model fine-tuning.
How do GPU clusters improve training speed?
Due to their parallel processing capabilities, GPU clusters can perform a higher volume of more complex mathematical calculations compared to CPUs. This means that for AI use cases, you can input more data and decrease overall training time.
Can I use multiple GPUs for distributed training?
Yes, you can use multiple GPUs for distributed training across nodes in a cluster, but it does require orchestration between nodes. It is also different from multi-GPU training, which is the use of multiple GPUs on a single node.
How does pricing work for GPU clusters?
Pricing for GPU clusters will vary from provider to provider. How much you spend depends on the type of GPU, the size of the cluster, whether the GPUs are on-demand or reserved, and the provider’s pricing structure. Most providers charge per hour per GPU.
Where are DigitalOcean GPU Droplets available?
GPU Droplets are available in our New York (NYC2) and Toronto (TOR1) data centers.