Spot GPU Droplets are in public preview. Spot GPU Droplet pricing is separate from on-demand pricing and may change daily based on available idle GPU capacity.
Bare Metal GPUs vs GPU Droplets
Last verified 11 Aug 2026
Bare Metal GPUs are dedicated, single-tenant servers with 8 GPUs of various models that can operate standalone or in multi-node clusters.
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 either without virtualization or with an LXC layer, 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 and Spot GPU Droplets with hourly billing. On-demand GPU Droplets provide guaranteed capacity at a fixed hourly rate. Spot GPU Droplets cost less but may be reclaimed with at least two hours’ notice. Both support variable usage and rapid scaling. | Contract-based billing and provisioning. Bare metal GPUs are more cost-effective for long-term, compute-intensive workloads that require consistent 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)