How to Create and Configure GPU Droplets

Validated on 26 Sep 2024 • Last edited on 26 Mar 2025

DigitalOcean Droplets are Linux-based virtual machines (VMs) that run on top of virtualized hardware. Each Droplet you create is a new server you can use, either standalone or as part of a larger, cloud-based infrastructure.

How to Use GPU Droplets

In general, you can manage GPU Droplets like non-GPU Droplets, but some features and requirements are specific to GPU Droplets:

Create GPU Droplets and choose the image, plan, datacenter, authentication method, and additional options.
Set up automatic mounting for the scratch disk on GPU Droplets.
Install the NVIDIA Data Center GPU Manager (DCGM) and DCGM Exporter to enable health monitoring, diagnostics, and process statistics for NVIDIA GPUs on GPU Droplets.
Enable jumbo frames, which are data packets with larger payloads than standard, on GPU Droplets.
1-Click Models, powered by Hugging Face, let you deploy third-party generative AI models on GPU Droplets with no additional setup and configuration.
This Community tutorial explains how to set up the NVIDIA container toolkit, run Docker for GPU workloads, and install Miniconda to manage Python environments on GPU Droplets.
digitalocean.com/community

GPU Droplets vs Bare Metal GPUs

DigitalOcean Bare Metal GPUs and GPU Droplets both provide GPU-based compute resources tailored to AI/ML workloads, but they’re each suited for different use cases.

Click to learn more about the difference between bare metal GPUs and GPU Droplets.
GPU Droplets Bare metal GPUs
Virtual machines. GPU Droplets have the convenience and ease of deployment that comes with managed infrastructure, but VM configuration is constrained by the hypervisor and shared OS layer. Physical servers. Bare metal GPUs are physical servers without virtualization, so you can set up advanced orchestration layers, containers, operating systems, and other deep configuration directly on the hardware.
Shared infrastructure. GPU Droplets share physical resources, so there may be minor resource fluctuations that don’t significantly impact tasks like fine-tuning and inferencing. Single tenant hardware. Bare metal GPUs are in isolated environments, which are best for use cases requiring full data isolation or highly consistent performance.
On-demand instances with per-hour billing. Pricing for GPU Droplets is flexible and low commitment, so GPU Droplets are best for variable usage or rapid scalability. Contract-based billing and provisioning. Pricing for bare metal GPUs is more cost effective, but meant for long-term use with intensive, prolonged workloads that need stable performance.

GPU Droplets are best for small- to medium-scale tasks, including:

  • Fine-tuning (adjusting models with specific data sets)
  • Inference (running predictions with high-speed responses for production applications)
  • Moderate data processing (lightweight analytics or video processing that benefit from GPU acceleration but don’t demand full hardware dedication)

Bare metal GPUs are best for advanced and custom workloads, including:

  • Model training at scale (training foundational models and handling large datasets with optimal performance)
  • Complex inference needs (running real-time inference for high-throughput applications)
  • Custom orchestration and HPC (like Kubernetes clusters, multi-node setups, or high-frequency trading)

If you have questions about GPU Droplets or require personalized guidance, contact our sales experts.

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