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AI & Machine Learning

GPU-accelerated dedicated servers for training and inference workloads. From NVIDIA L4 to H200, with high-memory configurations and fast interconnects.

Accelerate your AI and machine learning workloads with our GPU-dedicated server lineup, ranging from cost-effective NVIDIA L4 inference nodes to the flagship H200 NVL for large-scale model training. Each GPU server is paired with high-core-count AMD EPYC processors, up to 1TB of DDR5 memory, and NVMe storage arrays delivering sustained sequential throughput for dataset loading. Our GPU servers support all major ML frameworks out of the box, including PyTorch, TensorFlow, and JAX, with pre-installed CUDA toolkits and cuDNN libraries. For distributed training workloads, we offer multi-server GPU clusters connected via high-bandwidth, low-latency fabric, enabling efficient gradient synchronization across hundreds of GPUs. Flexible billing options let you scale from a single inference node to a full training cluster as your project demands.

Key Features

["NVIDIA L4, L40S, RTX 6000 Ada, H100 SXM, and H200 NVL GPUs", "Up to 1TB DDR5 RAM and high-speed NVMe storage arrays", "Pre-installed CUDA, cuDNN, and popular ML frameworks", "Multi-GPU cluster configurations with high-bandwidth interconnect", "Flexible hourly, daily, and monthly billing options", "Dedicated ML engineering support for cluster optimization"]

Get Started

Deploy your ai & machine learning infrastructure today with our global network of data centers.

Locations 10+
Uptime SLA 99.99
Support Premium.IT.HR@gmail.com
Network 100+ Tbps

Recommended Servers for AI & Machine Learning

Hand-picked server configurations optimized for ai & machine learning workloads.

AMD EPYC 4464P
Performance
CPU AMD EPYC 4464P
Cores 12C / 24T @ 3.7 GHz
RAM 64GB DDR5
Storage 2x 960GB NVMe
AMD EPYC 4585PX
Performance
CPU AMD EPYC 4585PX
Cores 16C / 32T @ 4.3 GHz
RAM 128GB DDR5
Storage 2x 1.92TB NVMe
AMD EPYC 9355
Performance
CPU AMD EPYC 9355
Cores 32C / 64T @ 3.55 GHz
RAM 128GB DDR5
Storage 2x 1.92TB NVMe

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