Bring your AI ideas to life with DigitalOcean GPU infrastructure
Accelerate your AI journey with high-performance GPU solutions purpose-built for generative AI (GenAI). Whether you're training large language models (LLMs), deploying multimodal AI systems, or running real-time inference, DigitalOcean provides flexible, cost-efficient GPU infrastructure that delivers the speed, scalability, and simplicity digital native enterprises and developers need.
Train smarter, deploy faster with GPU-powered GenAI
AI systems, from generative models like LLMs to multimodal agents, demand immense compute power to perform well at scale. But managing that infrastructure doesn’t have to be hard. DigitalOcean's cloud GPUs simplify how you build, fine-tune, and deploy GenAI applications. Run training jobs on high-performance NVIDIA GPUs like L40S and 6000 Ada Generation, fine-tune large models with multi-GPU acceleration, or serve real-time inference with low latency. With pre-configured environments and integrated ML frameworks, you can focus on innovation, not infrastructure.
Power GenAI with Gradient GPU Droplets
Take your GenAI models from development to deployment with GPU-accelerated virtual machines optimized for AI workloads. Whether you're training a computer vision model to detect manufacturing defects or running real-time inference for a customer chatbot, DigitalOcean’s Gradient GPU Droplets give you scalable, ready-to-launch compute for training, fine‑tuning, and inference, without infrastructure headaches.
Effortless setup
Go from zero to GPU‑powered compute in just two clicks or under a minute using pre-built AI/ML‑ready Droplet images with drivers, toolkits, and frameworks pre-installed. Choose single‑GPU or multi‑GPU (up to 8 GPUs) Gradient Droplets based on your compute needs, from lightweight fine‑tuning to full LLM training.
Choose your GPU flavor
AMD MI300X and MI325X (192 GB HBM3) delivers massive memory capacity for large model training with exceptional value. On the NVIDIA side, H100 (80 GB to 640 GB configurations) built on Hopper architecture, offers LLM training speed and mixed-precision efficiency, and the H200 improves bandwidth and memory throughput Ada‑based GPUs – RTX 4000 Ada, RTX 6000 Ada, and L40S (48 GB) are optimized for real‑time inference, rendering, and multimodal workloads.
Scale with predictability
Scale up to multi‑GPU nodes for parallel training or down for cost‑efficient inference. Whether you’re training LLMs with H100 or MI300X, deploying low-latency inference with L40S, or powering multimodal AI workflows on RTX 6000 Ada, you get the right performance profile for your GenAI applications.
GenAI framework integration
Comes with popular AI toolchains like CUDA, PyTorch, TensorFlow, Docker, and Hugging Face 1‑Click Models ready in inference-optimized images. Integrate with Kubernetes, Spaces, Volumes, APIs, and more, everything you need to build an end‑to‑end GenAI stack.
Build and deploy generative AI applications with GPU Droplets
DigitalOcean’s GPU Droplets offer the ideal infrastructure foundation for building and scaling generative AI applications, whether you’re customizing large models with proprietary datasets, generating real-time insights from user prompts, or integrating GenAI into product workflows. With support for powerful NVIDIA and AMD GPUs, pre-installed frameworks, and flexible scaling from 1 to 8 GPUs, you can move fast without managing complexity.
Fine-tune large language models with ease
Train and refine custom LLMs like LLaMA, Mistral, or Falcon using H100 or MI300X Droplets. Avoid memory bottlenecks and accelerate training with high-bandwidth, multi-GPU nodes.
Up to 1.5 TB GPU memory with MI325X-8.
Mix precision acceleration with H100 (Tensor Cores).
Pre-installed PyTorch and TensorFlow environments.
Run real-time GenAI inference at scale
Power chatbots, code assistants, or AI agents using inference-optimized GPUs like L40S or RTX Ada. Perfect for low-latency response generation, vision and text use cases, and more.
Support for serverless or always-on inference.
NVIDIA RTX 4000 Ada / 6000 Ada for lightweight
scalable performance.
Token-based billing ensures cost control for bursty workloads.
Train multimodal models with flexible storage
Use DigitalOcean GPUs to fine-tune or deploy models that combine image, text, and audio, ideal for content moderation, and generative media tools.
Attach Spaces object storage or Volumes block storage for large dataset ingestion.
Supports vision-language models like CLIP and Gemini-style architectures.
Easily scale up or down without setup delays.
Integrate with your GenAI toolchain
GPU Droplets come pre-loaded with popular AI stacks and integrate with your existing workflows—from version control to observability.
Docker/CUDA/cuDNN/NCCL and Fabric Manager pre-installed.
Works with GitHub/GitLab/VS Code and Hugging Face tools.
Compatible with GradientAI platform for multi-agent orchestration.
Resources to help you build
Deploy a multimodal AI chatbot that sees, listens, and responds in real time using OpenAI, Deepgram, and LiveKit on GPU Droplets.
Learn how GPU memory bandwidth affects performance in compute-intensive workloads, and why it matters for AI, gaming, and rendering.
Evaluate cloud GPU options based on workload type, cost, performance, and vendor flexibility to make the right infrastructure decision.
Build a multimodal bot that understands text, voice, and images using Django, GPT-4, Whisper, and DALL·E.
GPU for GenAI FAQs
What GPU is best for generative AI?
The best GPU for generative AI depends on your model size and workload. NVIDIA A100, H100, and L40S are top choices for training large language models and diffusion models due to their high memory bandwidth, tensor cores, and multi-GPU scalability. For inference or lightweight workloads, NVIDIA RTX 4000/5000 series or L4 GPUs offer a cost-effective balance of performance.
Do I need multiple GPUs for GenAI?
You’ll need multiple GPUs if you’re training large models like GPT, Stable Diffusion, or custom LLMs with billions of parameters. Multi-GPU setups help handle massive memory requirements and speed up training through parallelization. For fine-tuning smaller models or running inference, a single high-memory GPU (e.g., 24GB+) is sufficient.
Why does AI need GPU, not CPU?
AI workloads, like deep learning, rely on massive parallel processing for matrix operations and neural network computations. GPUs are designed with thousands of cores that can execute these operations concurrently, making them much faster than CPUs, which have fewer cores optimized for sequential tasks. This parallelism makes GPUs essential for training and inference in AI.
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Host your own GPU with DigitalOcean.
