GPU Server Rental

Renting vs. buying a GPU server

The first decision in GPU infrastructure is whether to buy the hardware or rent it. Both approaches make sense in the right situation; what decides it is how long the workload will run, how intensively the hardware will be used, and whether your team has the capacity to operate the hardware itself.

Criterion	Renting a GPU server	Buying a GPU server
Upfront investment	Low, periodic payments	High, full hardware cost upfront
Hardware obsolescence	The provider’s responsibility	Your organization’s responsibility
Power and cooling	Included in the data center infrastructure	Must be planned separately; GPUs draw a lot of power
End of project	Resources can be scaled down or shut off	Risk of idle hardware
Best fit	Pilot projects, variable or seasonal workloads	Continuous, fully utilized use over several years

How a GPU differs from a CPU, and which workloads it excels at, is covered in detail in our guide What is a GPU server?

Looking only at the hardware price can be misleading. For a purchased GPU server, you also need to account for power, cooling, spare parts, driver and firmware updates, and the staff who will respond when something fails. If the project is still in the exploration phase and it is not yet clear at what scale the model will go into production, starting with a rental and making a long-term decision once your usage profile is clear reduces risk. For projects where corporate data should not be sent to a platform outside the country, rented GPU infrastructure hosted in Türkiye is an additional reason to choose this route.

Service assurance: Support for GPU servers follows the priority levels in our service level agreement; for a critical failure that stops a training or rendering job, the first-response target is 15 minutes. The infrastructure where model and training data are processed is managed under an ISO/IEC 27001 certified information security management system (see our certifications).

To see the GPU memory a model needs before requesting a quote, use the GPU VRAM calculator; it shows the requirement by model, precision and concurrent users, which speeds up choosing the card class and count.

The right configuration for model training and inference

Even within the same project, the training and inference phases need different resources. For training, what matters most is GPU memory (VRAM), the number of GPUs and GPU-to-GPU communication; large models are trained by splitting them across multiple GPUs. For inference, per-request latency and the number of concurrent users take priority; a smaller configuration that runs continuously is often enough.

Getting full efficiency out of a GPU also depends on the components around it. A disk that cannot read the dataset fast enough, or insufficient system memory, leaves expensive GPU capacity sitting idle. On the software side, compatible driver, CUDA and framework (PyTorch, TensorFlow) versions are critical for performance and stability; defining this environment with container images keeps development and production consistent. A common architecture pairs the GPU server with S3-compatible object storage for large datasets and model checkpoints.

What drives GPU server pricing

• GPU class and memory capacity: the model and the data batch need to fit in VRAM.
• Number of GPUs: a single-GPU development environment and a multi-GPU training cluster are on very different scales.
• CPU, RAM and NVMe storage: these determine how fast data is prepared and loaded.
• Duration of use: a short-term project and long-term steady use are planned differently.
• Management and backup scope: environment setup, monitoring and protection of model and data backups.
• Network and access: how the dataset is transferred to the server, private access over VPN, and security rules for externally exposed inference services.

Sharing the model family, approximate parameter size, dataset volume and target training or response time before requesting a quote helps avoid choosing a configuration that is larger or smaller than you need. Taking measurements on a single GPU during the pilot and scaling up based on the results is a more controlled path, especially for teams running their first AI project.

GPU	Monthly starting price
NVIDIA RTX 5090	From TRY 35,000
NVIDIA L40S	From TRY 39,000
NVIDIA H200	From TRY 179,000
NVIDIA B200	From TRY 289,000

Prices vary with CPU, RAM, disk, traffic, power consumption and requested add-on services; card availability and delivery time are confirmed at the quote stage.

AI server rental: matching the GPU class to the workload

A GPU server is a server with one or more graphics processors that run thousands of operations in parallel, which is why it is used for AI, rendering and simulation. For AI server rental, the right card depends less on the model’s popularity and more on how much memory the job needs and whether it runs as a one-off training job or as a service that answers users all day.

Three AI workload types tend to come up in practice. Fine-tuning adapts an existing open model to company documents or a specific task; memory needs grow with parameter count and precision, and parameter-efficient methods can bring the job down to a single card. Inference and model serving covers chatbots, document search and classification APIs; concurrent users, context length and response time set the limits, and because the server runs continuously, steady cost matters more than peak speed. Training from scratch is rare outside research teams and needs multiple data-center GPUs with large memory and fast GPU-to-GPU links.

In the MAV Cloud range, workstation-class cards such as the RTX 5090 suit development, smaller models and pilots; the L40S is a data-center card that handles both inference and graphics work; H200 and B200 are aimed at large models whose weights and batches need high-bandwidth memory. As a reference point, monthly pricing starts from TRY 35,000 for an RTX 5090 configuration. Model size and user count decide the class, not the card name.

GPU servers for rendering: 3D, animation and video

A GPU render server shortens frame and scene render times by moving work from the CPU to GPU-accelerated render engines. Using a GPU server for rendering makes most sense when render queues block artists, or when deadlines create short peaks that do not justify buying hardware.

For rendering, the checks are different from AI work. The full scene, textures and geometry must fit into the card’s memory; if they do not, some engines fall back to slower modes or fail the job. Render engine and plugin compatibility with the driver version should be confirmed before a project starts, and licensing for render nodes is checked with the software vendor. Video work adds hardware encoding and fast storage for large source files to the list. Because project files are often hundreds of gigabytes, the upload method, the storage tier next to the GPU and the way finished frames are returned also need planning.

Renting a dedicated GPU server: what to check

When you rent a dedicated GPU server, the card model is only one line of the specification. A short checklist avoids most surprises in GPU server rental:

• Dedicated vs shared: whether the GPU is assigned to a single customer or sliced between several.
• Surrounding hardware: CPU cores, system RAM and NVMe capacity sized so the GPU is not left waiting for data.
• Software stack: operating system, driver, CUDA and framework versions, and who maintains them.
• Data location and access: where training data and model files are stored, VPN or private access, and firewall rules for public inference endpoints.
• Support terms: first-response targets in the service agreement; at MAV Cloud these are 15 minutes for critical, 1 hour for high, 4 hours for medium and 1 business day for low priority.
• Availability: card availability and delivery time are confirmed at the quote stage, so timelines should be agreed before a project date is fixed.

To estimate the VRAM a model needs before choosing a card, use the GPU VRAM calculator; the background on GPU architecture is covered in what is a GPU server.