Train smarter, faster AI systems with multi-GPU acceleration on DigitalOcean
Deploy faster, scale effortlessly, and power high-performance AI workloads with flexible, cost-efficient multi-GPU deployments that are fully supported on GPU Droplets.
Build without boundaries using multi-GPU Droplets
Whether you're fine-tuning large language models (LLMs), training multimodal models, or running high-throughput inference, your workloads demand more power. DigitalOcean’s flexible GPU infrastructure supports multi-GPU Droplets that optimize deep learning and high-performance workloads, without the cost or complexity of traditional setups.
Scale AI workloads with multi-GPU power
Use GPU Droplets on DigitalOcean to distribute and deploy your training, inference, and compute-heavy tasks across multiple GPUs, without the complexity.
Train faster
Distribute training across multiple GPUs to cut down model training time for large-scale AI/ML, deep learning, diffusion models, and generative tasks. Easily scale up as your model grows.
Run parallel inference
Serve multiple concurrent requests by running inference in parallel across GPUs. Boost throughput for real-time AI applications like gaming, chatbots, vision systems, and recommendation engines.
Flexible framework support
Integrate popular ML frameworks like Hugging Face with built-in multi-GPU compatibility, no need for special configurations.
Launch in minutes
Spin up multi-GPU environments on demand using our intuitive UI or API. No need to manage complex infrastructure or network setups.
Scale smarter, spend less
Scale vertically or horizontally based on your budget and workload needs. Lower your cloud spend with predictable pricing and savings, up to *75% vs. leading hyperscalers.
*Up to 75% cheaper than AWS for on-demand H100s and H200s with 8 GPUs each. As of April 2025.
Learn more about DigitalOcean multi-GPU solutions
Our GPU Droplets are designed to accelerate your most demanding AI, ML, and high-performance computing (HPC) workloads. Whether you're training massive LLMs, running high-throughput inference, or processing large datasets, you can scale effortlessly across single- or multi-GPU setups, on an infrastructure trusted by scaling startups and established enterprises.
Scale from 1 to 8 GPUs
Choose from single GPU setups to 8x H100s, MI300Xx8 or MI325Xx8 - to match the compute intensity of your workloads.
Flexible vertical and horizontal scaling.
Up to 1.5 TB of GPU memory for massive workloads.
Built for artificial intelligence, machine learning, and high-performance computing
GPU Droplets support popular frameworks like PyTorch, TensorFlow, and Hugging Face for LLM training, image generation, and data analytics.
Pre-installed Python and deep learning stacks.
HIPAA-eligible and SOC 2 compliant.
Predictable pricing, no surprises
Avoid bill shock and run your AI workloads on top-tier GPUs at a fraction of the cost of hyperscalers like AWS, Google Cloud, and Microsoft Azure. Transparent billing and flexible scaling mean you only pay for what you need.
Pay only for what you use.
No vendor lock-in.
Simple setup, production-ready deployment
Spin up a GPU Droplet in under a minute via API or dashboard. Integrate with DigitalOcean Kubernetes, GenAI Platform, and your existing workflow.
2-click launch.
10 Gbps public / 25 Gbps private bandwidth.
Resources to help you build
Understand how multi-GPU systems work and when to use them for AI, ML, and rendering workloads.
Master techniques like model and data parallelism to scale LLM training efficiently.
Learn how to use data/model parallelism and avoid out-of-memory errors in PyTorch.
Use Hugging Face’s Accelerate library to simplify distributed training in PyTorch.
FAQs
What is a multi-GPU setup?
A multi-GPU setup uses two or more GPUs within a single system or across connected nodes to parallelize compute tasks. This is useful when you build large-scale AI/ML workloads like training LLMs, performing batch inference, or running HPC jobs. Multi-GPU setups help you reduce processing time and improve throughput by distributing work across available GPUs.
How do I choose between GPU Droplets and Bare Metal for a multi-GPU setup?
GPU Droplets are ideal if you want to get started quickly, scale on demand, and manage everything through a simple cloud dashboard or API. They’re perfect for AI/ML use cases like fine-tuning models, serving inference, or running experiments.
DigitalOcean Bare Metal GPUs offer dedicated, single-tenant access to the full underlying hardware, suitable for large-scale model training, custom orchestration, and deep optimization at the OS, driver, or library level. With up to 8 GPUs per system, 1.5 TB of GPU RAM, and 2 TB of system memory, Bare Metal gives you maximum performance, privacy, and control for demanding workloads like LLM pretraining, multimodal generation, and complex distributed training.
Can I fine-tune LLMs on a multi-GPU cluster?
Do I need a containerized setup to scale across GPUs?
Containerization is recommended but not required. Running containerized workloads with orchestration tools like Kubernetes makes it easier to manage, schedule, and scale multi-GPU jobs across nodes. However, you can also run multi-GPU training or inference directly on GPU Droplets using native Python scripts, libraries, or frameworks like PyTorch and TensorFlow.
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Get started with hosting multi-GPUs with DigitalOcean today.
