GPU Servers

A regular Dedicated server is mainly CPU-oriented. A GPU server additionally gives you VRAM and thousands of parallel compute cores, which is much more effective for AI and rendering tasks.

The choice depends on model size, VRAM requirements, inference/training type, and the number of concurrent requests. T4/L4 may be enough for small inference, while a larger LLM or training workload requires an A100/L40S-type GPU.

Yes, initial preparation of NVIDIA Driver, CUDA Toolkit, runtime, and Docker GPU environment is available on request. After that, daily server management is handled by the customer through full Root/Admin access.

Yes, on a GPU server you can run PyTorch, TensorFlow, JupyterLab, a Python virtual environment, or a Docker container workflow. Initial stack setup is available by prior agreement.

Yes, a GPU server can be used for Blender, 3D rendering, architectural visualization, animation, and video processing tasks. When choosing a configuration, GPU model, VRAM, and storage speed are important.

Not necessarily. A GPU server can be Dedicated Bare Metal or a virtualized GPU resource. This page focuses on dedicated/high-performance GPU infrastructure.

VRAM is GPU memory where the model, batch data, or render workload is stored. If VRAM is insufficient, the job may fail to run or slow down significantly.

Yes. On a GPU server, the customer has full Root/Admin access. This means you can manage the OS, software stack, firewall, access policy, deployments, and configuration of all internal services yourself.

Yes, Docker, Docker Compose, NVIDIA Container Toolkit, and, if needed, a Kubernetes node can be prepared. The architecture must be agreed in advance based on the workload.

Monitoring is available as an additional option: GPU utilization, VRAM, temperature, CPU/RAM, disk, network, and core services. By default, the server is delivered with Root/Admin access for independent management.

Backup is configured according to agreed storage, frequency, and retention. For large datasets, a separate storage policy is often used because a full daily backup of all data is expensive and inefficient.

It depends on the workload. If you need GPU constantly, renting is often more predictable than usage-based cloud costs. For short experiments, cloud may be more flexible.

Yes, LLM inference, RAG, embedding, and private AI assistant workloads are possible. GPU choice depends on model size, quantization, concurrency, and latency requirements.

In the first stage, send us a description of the workload: what software/model you need, how much VRAM is required, how much storage you need, and what uptime requirement you have. After that, we will select the configuration and launch plan.