Cheap GPU Dedicated Servers

GPUs deliver massively parallel computing power that CPUs simply cannot match for certain workloads. A single NVIDIA GPU contains thousands of CUDA cores that can process matrix operations, neural network layers, and parallel computations orders of magnitude faster than even the most powerful CPU. For AI/ML training, 3D rendering, and video encoding, GPU servers are not just faster — they are essential.

Cloud GPU instances from major providers cost $1 to $30+ per hour, making sustained workloads extremely expensive. A dedicated GPU server at $149/mo costs less than running a comparable cloud instance for just a few hours per day. If your GPU workloads run consistently, dedicated hardware pays for itself within the first week of the month.

Dedicated GPU servers also eliminate the unpredictability of cloud GPU availability. Popular GPU instances are frequently sold out or require reservations months in advance. With a dedicated server, your GPU hardware is available 24/7 — no spot instance interruptions, no capacity limitations, no bidding wars.

Our entry-level GPU server at $149/mo includes an NVIDIA Tesla T4 or equivalent GPU with 16 GB VRAM, paired with an Intel Xeon or AMD EPYC processor, 64 GB ECC RAM, and NVMe SSD storage. The Tesla T4 provides excellent inference performance and supports mixed-precision training for smaller AI/ML models.

Mid-range configurations feature NVIDIA RTX 3090, RTX 4090, or Tesla V100 GPUs with 24 to 32 GB VRAM. These servers handle training medium-sized neural networks, professional 3D rendering with tools like Blender and Maya, and high-throughput video transcoding with hardware-accelerated encoding.

For serious AI research and large model training, we offer multi-GPU configurations with 2x or 4x NVIDIA A100 or H100 GPUs connected via NVLink for high-speed inter-GPU communication. These servers tackle large language model fine-tuning, computer vision training on massive datasets, and other workloads that require multiple GPUs working in parallel.

Our GPU servers come pre-installed with NVIDIA CUDA drivers and cuDNN libraries, or you can install your preferred driver version manually. All major deep learning frameworks — PyTorch, TensorFlow, JAX — run natively on our hardware with full CUDA acceleration. Docker containers with GPU passthrough are fully supported for reproducible ML environments.

For rendering workloads, our GPU servers support Blender Cycles, OctaneRender, V-Ray GPU, Redshift, and other GPU-accelerated renderers. Video production teams use our servers for real-time video encoding with NVENC, batch transcoding with FFmpeg GPU acceleration, and AI-powered video upscaling and restoration.

Scientific computing applications including molecular dynamics (GROMACS, AMBER), computational fluid dynamics, and financial modeling benefit from CUDA acceleration on our GPU servers. Any application that supports NVIDIA GPU computing will run on our hardware with full performance and driver compatibility.

Select your GPU model and quantity, choose your CPU, RAM, and storage configuration, and complete payment. GPU dedicated servers are provisioned within 24 to 48 hours as we verify GPU functionality and driver compatibility before delivery. Custom multi-GPU configurations may take up to 72 hours.

Every GPU server includes IPMI remote management with GPU temperature monitoring, full root access, and your choice of Linux distribution. We pre-install CUDA drivers by default, but you can reinstall with any driver version through the control panel. Windows Server is also available for applications that require it.

Our support team includes engineers experienced with GPU server workloads. We can assist with CUDA installation, driver troubleshooting, multi-GPU configuration, and performance optimization. Monthly billing with no contract — test our GPU servers for your workload and scale up or cancel as needed.