Bare Metal GPUs vs GPU Droplets
Validated on 18 Feb 2025 • Last edited on 18 Apr 2025
DigitalOcean Bare Metal GPUs are dedicated, single-tenant servers with 8 GPUs of various models that can operate standalone or in multi-node clusters.
DigitalOcean Bare Metal GPUs and GPU Droplets both provide compute resources tailored to AI/ML workloads, but they’re each suited for different use cases.
| 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)