AI-ready
GPU Servers

A GPU server is a cloud server equipped with a dedicated graphics processing unit alongside its CPU, memory and storage. While originally designed for rendering graphics, GPUs excel at parallel processing, performing thousands of calculations simultaneously. This makes them well suited to workloads like AI model training, machine learning inference, video transcoding and scientific computing, where the same operation runs across huge amounts of data at once.

A regular cloud server relies on CPUs, which handle a wide range of tasks sequentially and are ideal for general workloads like web hosting, databases and applications. A GPU server adds a graphics processor with thousands of smaller cores built for parallel work. For tasks like training neural networks or processing video, a GPU can be orders of magnitude faster than a CPU alone. Many teams run both, using Cloud Servers for their applications and GPU Servers for compute-heavy jobs, connected over the same private network.

Common uses include training and fine-tuning machine learning models, running inference for AI applications like chatbots and recommendation engines, video transcoding and streaming, 3D rendering, and scientific simulations. Generative AI has driven much of the recent demand, from serving large language models to image and video generation. See our AI/ML solutions and Audio/Video solutions pages for detail on specific workloads.A

For small models and light experimentation, a CPU can be enough. But for training models of any real size, or serving inference at production speed, a GPU is effectively essential. Even running open-weight models like Llama or Mistral locally becomes impractical on CPU beyond the smallest sizes. A single L4 or L40S server is a sensible starting point for most inference workloads, and you can scale up from there. Our Ollama tutorial shows how to get a model running in under an hour.

Yes. GPU Servers are billed hourly with no long-term commitment, so you can spin one up for a training run and delete it when finished. For maximum savings on interruptible workloads, spot pricing offers the same hardware at a significant discount. Monthly costs are capped at 28 days of usage, so a server running all month never costs more than the listed monthly price.

It depends on the workload. The NVIDIA L4 is the most cost-effective option for AI inference, video transcoding and edge deployments. The L40S is a versatile all-rounder suited to generative AI, mid-scale model training and 3D rendering. The H100 handles high-traffic inference and large model training, while the B200 is built for the largest jobs, including trillion-parameter models. You can also switch between GPU models later by shutting down the server and changing the plan, so you are not locked into your first choice.

For more guidance, see our AI/ML GPU solutions and Audio/Video GPU solutions pages.

All GPU Server plans run in our data center in Helsinki, Finland. The facility is powered entirely by renewable energy and recovers up to 90% of waste heat, which is fed into Helsinki’s district heating network. Read more about our approach on the EU Sovereign Cloud page or explore our data centers.

GPU Servers deploy in just a few minutes through the control panel or API. Our AI/ML-ready Ubuntu template comes with NVIDIA drivers and tooling pre-installed, so you can go from deployment to running workloads without manual setup. If you want a practical walkthrough, our tutorial on running LLMs with Ollama takes you from a fresh server to inference on an open-weight model.

GPU Servers can be attached to your infrastructure alongside our other products and connected over SDN private networks. If you are orchestrating containerised workloads, see Managed Kubernetes for running clusters alongside your GPU capacity.

By using our AI/ML-ready GPU Ubuntu template, the necessary NVIDIA drivers are pre-installed. If you choose another operating system, you will need to manually install the appropriate NVIDIA drivers and CUDA toolkit to enable GPU functionality.Q

No. All GPU Server plans include zero-cost egress, so there are no charges for outbound data transfer. This matters for AI workloads where moving datasets, model weights and inference results in and out of the cloud can otherwise become a significant cost. 

Our Fair Transfer Policy applies.

Multi-GPU servers are equipped with more than one dedicated GPU. All GPUs are exposed to your server via PCIe passthrough, allowing you to utilize them for parallel processing, distributed training, or other multi-GPU workloads. You can verify the available GPUs using the nvidia-smi tool.

Yes, each GPU is dedicated to a single server and is not shared with other customers. This ensures consistent performance and security for your workloads.

Our advice is to match the GPU to the job.  You can find a great comparison between our GPUs >here<

• For inference on small to mid-sized models, the L4 offers the best price to performance. 
• The L40S steps up to generative AI, fine-tuning and mid-scale training. 
• The H100 suits large model training and high-throughput inference
• The B200 is for the most demanding jobs, including training and serving the largest models. 

Memory is usually the deciding factor: your model weights, plus overhead, need to fit in GPU memory for inference, and training needs considerably more. If in doubt, start smaller. You can shut down and switch to a bigger GPU without reinstalling.

Beyond the GPU itself, consider how you deploy it. GPU Servers on hourly billing suit most teams, Managed Kubernetes works for containerised workloads across multiple nodes, and Private Cloud GPUs provide dedicated hardware for organisations with strict compliance or isolation requirements. 

If you are unsure, start with a single GPU Server and contact our sales team.

Spot pricing is good for time-insensitive workloads, sold at a discount. A spot instance may be terminated at any given time. Read more at GPU Server Spot pricing documentation.

GPU virtualization is how a physical GPU is made available to virtual servers. There are two main approaches. Shared virtualization splits one GPU between multiple customers, which lowers cost but means unpredictable performance. Passthrough gives a virtual server direct, exclusive access to the physical GPU. UpCloud GPU Servers use passthrough, so the full performance of the GPU is yours alone, with no sharing and no contention from other tenants. You get bare-metal GPU performance combined with the flexibility of a cloud server.

Yes. If you need isolated infrastructure for compliance or security reasons, Private Cloud GPUs provide dedicated GPU hardware within your own private cloud environment. This suits organisations with strict data handling requirements, including public sector and regulated industries.