Turkey (Türkçe) 
Someone searching for "graphics card rental" may actually be looking for one of three different things: an hourly cloud GPU, a GPU attached to a virtual server, or a physical server with a graphics card reserved for them alone. All three are sold under the "GPU rental" label, yet their performance, level of control and cost behaviour are very different. This guide explains what a rented graphics card means in practice, how the three delivery models differ, how to think about renting versus buying, and how to choose the right card for your workload. The goal is to help you ask the right questions before you order.
A graphics card is not rented as a standalone product, because it only makes sense inside a machine: on a motherboard PCIe slot, next to a strong processor, enough memory, fast storage and a network connection. So when people say "rent a graphics card" they really rent a server that has a graphics card in it. You connect over SSH or Remote Desktop, install the NVIDIA driver and CUDA, and run your AI model, render job or game server.
The difference from doing this on a home computer is continuity and infrastructure. A data center has redundant power, cooling and networking, the server stays on around the clock, and a remote management console lets you reach the hardware. You do not deal with sleep modes, dropped home connections or overheating in a small room.
Services sold as GPU rental fall into three main models. Which one suits you depends on how long the workload runs and how predictable you need performance to be.
| Feature | Hourly cloud GPU | GPU VDS | Physical GPU server |
|---|---|---|---|
| Billing | Per hour or per minute | Usually monthly | Fixed monthly |
| Who owns the GPU? | Assigned while you use it | May be shared or partitioned | The card is reserved for you |
| Performance consistency | Depends on the provider | Can fluctuate with neighbouring load | Constant; VRAM and CUDA are yours |
| Control | Mostly image and container based | Virtual machine level | Full root, driver and operating system |
| Remote hardware access | Usually none | Limited | IPMI/KVM console |
| Best fit | Short, occasional experiments | Light and trial workloads | Inference, rendering and game servers that need continuity |
The appeal of the hourly model is flexibility: billing stops when the job ends. But if you keep the same machine on for most of the day, hourly accounting gets complicated and the monthly total becomes unpredictable. A GPU VDS is a cheap entry point for small experiments; we covered the details in our GPU VDS explained article. When you want continuity and stable performance, a physical GPU server stands out.
Buying a powerful graphics card and fitting it into your own server is also an option. Think about the whole set of costs rather than one line:
A simple evaluation works like this: write down the total purchase investment plus monthly electricity and internet costs; multiply the monthly rental fee over the same period; then compare both totals. If you are unsure how long you will use the card, or the project is short, renting is usually the lower-risk option. If you will run the same card at full load for several years, the purchase calculation is worth doing too. You can see current package prices on the GPU server rental page.
VRAM, the card's own memory, is the biggest factor in choosing the right card. If the model, image pipeline or scene does not fit in memory, the job fails or slows down badly. These are the cards in SistemDC GPU server packages and their typical uses:
| Card | VRAM | Typical use |
|---|---|---|
| RTX 5060 | 8 GB | Entry level: small models, basic image generation, game servers, VDI |
| RTX 5060 Ti | 16 GB | Mid-size models, SDXL-based workflows, everyday inference |
| Tesla P40 | 24 GB | Training and inference that need a lot of VRAM; older architecture |
| RTX 5090 | 32 GB | High-performance rendering, large model inference, high-resolution generation |
| Dual RTX 5060 Ti and Dual RTX 5090 | Two cards | Two separate workloads, or splitting one model across two cards |
Because the Tesla P40 is an older generation, it lacks the fast half-precision throughput of newer cards; in return its 24 GB of VRAM makes it a sensible option for large models that must fit in memory. For a closer look at each card, read our RTX 5090 and Tesla P40 guides.
Choose your GPU server in our Istanbul data center
Delivered with a physical GPU, full root access and Gen4/Gen5 M.2 NVMe storage. For several servers or a custom configuration, use the quote form.
Browse GPU server packagesWhen your server is delivered, first verify the hardware and the driver. After connecting on Linux, these commands give a quick health check:
nvidia-smi
lspci | grep -i nvidia
free -h
lsblk
df -hnvidia-smi shows the card name, the amount of VRAM, the driver version and the temperature. If the command fails, the driver may not be installed yet; on Ubuntu, ubuntu-drivers devices lists the recommended package for your card. Then update the system, define a firewall rule and switch SSH to key-based access. Our Linux server security guide covers these steps.
The model and data you run on a rented server are your responsibility; the provider supplies the infrastructure and you build the application security. In practice three habits prevent most problems. First, allow SSH with keys only and turn off password logins. Second, instead of exposing model servers and web interfaces directly to the internet, bind them to the local address only and use an SSH tunnel or an authenticated reverse proxy when you need access. Third, take regular backups and keep them elsewhere; model files can be downloaded again, but your own data and fine-tuning output cannot.
Where the data sits matters too. If you will process documents that contain personal data with an AI model, keeping the data on a server in Turkey reduces questions about cross-border transfer. That calls for a legal assessment; on the technical side, a locally running model means the data is not sent to a third-party AI service. We cover the topic in more detail in our KVKK and Turkey-located server guide; this article is not legal advice.
You can narrow down your situation with a few questions. If your workload is a small model that fits in 8 GB or a light image generation pipeline, an entry-level card is enough. Mid-size models that need 16 GB of VRAM and services used daily are more comfortable one step up. If you will run a large language model in one piece or generate at high resolution, look at the 24 and 32 GB cards. If you will run several independent jobs at the same time, dual-card packages make sense.
If you are unsure, a month gives you enough information: watch GPU utilization with nvidia-smi, see how much of the VRAM fills up, and find out whether the bottleneck is the card, the memory or the disk. Resizing the package up or down afterwards is easy. If your needs do not fit the ready-made packages, you can request a custom configuration through the quote form on the page.
Need a custom GPU configuration?
For several servers, multi-GPU setups or a long-term contract, fill in the quote form and our sales team will get back to you.
Go to the GPU server quote formCan I rent a graphics card on its own?
Usually not. A graphics card works inside a server, so what you rent is a complete machine with the card installed, plus its CPU, memory, storage and network connection. In practice, renting a graphics card means renting a GPU server.
What is the difference between a GPU server and a GPU VDS?
A GPU VDS is a virtual server where the GPU resource may be shared or partitioned. On a physical GPU server the card belongs only to you, so VRAM and CUDA performance stay constant and you manage the driver and operating system with full control.
When is monthly rental more sensible than an hourly cloud GPU?
If the machine will run most of the day, for example 24/7 inference, a constant render queue or a game server, a fixed monthly arrangement is easier to predict. Hourly models are more flexible for short, occasional experiments.
Which graphics card is enough to start with?
For small models, basic image generation workflows and game servers, cards with 8 to 16 GB of VRAM are enough to start. For large language models and high-resolution generation, look at cards with 24 and 32 GB of VRAM.
How do I connect to a GPU server remotely?
You connect with SSH on Linux and Remote Desktop on Windows. A browser-based IPMI/KVM console also lets you reach the machine's screen when there is a network or operating system problem.
Can I install my own software on a rented GPU server?
Yes. A physical GPU server gives you full root access, so you can install the NVIDIA driver, CUDA, Docker and your own applications as you wish.
What should I check before ordering a GPU server?
Start with the amount of VRAM, system memory, NVMe capacity, the network connection and traffic policy, operating system options, remote management and support channels.
