Powerful Dedicated GPU Servers for AI & HPC

A Dedicated GPU Server is a physical server equipped with one or more Graphics Processing Units (GPUs) for accelerated computing tasks like AI, machine learning, deep learning, rendering, and scientific simulations. Unlike shared cloud instances, a dedicated GPU server provides exclusive access to GPU resources.

Consider factors such as the type of applications you’ll run (gaming, AI, rendering), budget, and desired performance level. You can reach out to our support team for personalized recommendations.

• High Performance – GPUs accelerate computation-intensive tasks significantly.
• Exclusive Resource Allocation – No sharing of GPU power, ensuring maximum efficiency.
• Low Latency – Optimized for real-time processing and deep learning workloads.
• Better Cost Efficiency – Compared to cloud-based GPU instances for long-term projects.

• AI & Machine Learning – Training large neural networks.
• Video Rendering & VFX – High-end animation and 3D modeling.
• Big Data & Analytics – GPU acceleration for large-scale data processing.
• Gaming & Game Development – Game physics simulations and real-time rendering.

Popular GPUs for dedicated servers include:

• NVIDIA A100, H100 – AI/ML workloads, high-performance computing
• NVIDIA RTX 4090, 3090 – Rendering, deep learning, gaming
• AMD Instinct MI250 – HPC, AI applications

Most GPU servers support:

• Linux (Ubuntu, CentOS, Debian) – Preferred for AI/ML workloads
• Windows – For game development, rendering, and enterprise applications
• Custom OS – Depending on the provider and user requirements

GPU passthrough allows a physical GPU in a dedicated server to be assigned to a virtual machine (VM), enabling direct GPU access.

• Used in cloud gaming, AI research, and virtual desktop infrastructure (VDI).
• Requires SR-IOV (Single Root I/O Virtualization) or NVIDIA vGPU technology.
• Helps in multi-tenant environments where multiple users need isolated GPU access.

Common hypervisors that support GPU passthrough: VMware ESXi, Proxmox, KVM/QEMU, Hyper-V.

Consider the following factors:

• Purpose – AI/ML (NVIDIA A100, H100), Rendering (RTX 4090, Quadro), HPC (AMD Instinct MI250).
• Memory (VRAM) – AI workloads need 24GB+ VRAM (e.g., NVIDIA A100 80GB).
• CUDA Cores / Tensor Cores – More cores = better performance for AI training.
• PCIe Bandwidth – Higher bandwidth ensures optimal data transfer between GPU and CPU.
• Software Compatibility – Ensure support for CUDA, ROCm, or OpenCL depending on your workload.

Yes! Many dedicated GPU servers support multi-GPU configurations. Benefits include:

• Distributed Computing – Parallel model training using frameworks like PyTorch DDP.
• NVLink / PCIe Scaling – High-speed interconnects allow GPUs to share memory and increase efficiency.
• Better Performance for Large Models – AI models like GPT and Stable Diffusion benefit from multi-GPU setups.
• PCIe Bandwidth – Higher bandwidth ensures optimal data transfer between GPU and CPU.

Common setups include dual-GPU (2x RTX 4090) or enterprise-grade 4x NVIDIA A100 servers.

The amount of RAM depends on your workload:

• AI/ML – At least 64GB RAM, preferably 128GB+ for large datasets.
• Rendering/VFX – 32GB-128GB based on scene complexity.
• Gaming Servers – 16GB-64GB depending on the game engine and number of users.

A good rule: RAM should be 2-4 times the GPU VRAM for optimal performance.

It depends on the model size and budget:

• Entry-Level – RTX 4090 / RTX 3090 (Good for small ML models).
• Mid-Range – NVIDIA A100 40GB (Excellent for most AI/ML workloads).
• High-End – NVIDIA H100 / DGX A100 (Best for LLMs and complex AI training).

For large-scale AI, multi-GPU clusters with NVLink or Infiniband are recommended.

A dedicated GPU significantly outperforms a CPU for AI workloads due to:

• Parallel Processing – GPUs have thousands of cores optimized for matrix calculations, accelerating deep learning.
• Higher Throughput – Can process multiple tasks simultaneously, unlike CPUs which handle sequential tasks.
• Optimized Libraries – AI frameworks like TensorFlow, PyTorch, and CUDA leverage GPU acceleration for training and inference.
• Faster Model Training – Reduces training time from weeks to hours compared to CPUs.