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GPU server cost is not determined by simply having graphics. GPU model, VRAM, CPU, memory, NVMe and physical allocation matter. Many current EKA Sunucu catalog entries use integrated Radeon graphics; projects requiring CUDA should separately verify NVIDIA/CUDA availability.
On 18 August 2026, graphics/physical-server listings include 1,750 TRY/month Athlon 3000G + Vega 3, 2,950 TRY/month Ryzen 5 5600G, 5,000 TRY/month Ryzen 5 8600G + Radeon 760M and a 13,850 TRY/month high-end 64 GB/2 TB NVMe configuration. Some plans are out of stock; verify availability before ordering.
Availability must be checked separately. Prices describe hardware configurations; AI performance also depends on GPU architecture and software compatibility.
If your TensorFlow/PyTorch project requires CUDA, integrated Radeon graphics are not equivalent to NVIDIA CUDA. Confirm whether the workload needs ROCm, DirectML, OpenCL or CUDA.
Hardware choice should follow software APIs and memory requirements.
Commands vary by GPU vendor. PCI device and OpenGL/OpenCL visibility are useful first checks.
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lspci | grep -Ei 'vga|3d|display'glxinfo -B 2>/dev/null | head -n 20clinfo 2>/dev/null | head -n 40nvidia-smi 2>/dev/null || truerocminfo 2>/dev/null | head -n 30 || trueHigh CPU, memory and storage do not help if the GPU does not match the framework acceleration path.
Lower cost, limited compute
CPU + graphics balance
Choose by VRAM/API support
As of 18 August 2026, EKA Sunucu lists graphics-server options starting at 1,750 TRY/month; availability can change.
The listed model uses AMD Athlon 3000G with Radeon Vega 3 graphics. A CUDA-dependent workload requires separate NVIDIA/CUDA verification.
It depends on model size, quantization, context and concurrency; calculate the target model's real VRAM demand first.
Share the LLM, renderer, video or graphics application; we can verify CUDA/ROCm/OpenCL and VRAM requirements before sizing hardware.