GPU & AI Servers
Our GPU Servers range provide high-performance, energy-efficiency and top-quality renders. They are engineered for the most demanding work fields and can run any type of heavy-data at high-speed.
Our GPU & AI servers are dedicated high-performance computing systems designed for the most compute-intensive workloads, from large-scale AI model training to real-time inference, HPC simulations, and 3D rendering. Engineered with NVIDIA® and AMD® certified components, they combine raw processing power, advanced cooling technologies, and energy-efficient architecture to meet the demands of modern data centers.
A GPU server is a high-performance computing system that integrates one or more Graphics Processing Units (GPUs) alongside standard CPUs.
Unlike traditional CPU-only servers, GPU servers are purpose-built to handle massively parallel workloads, processing thousands of operations simultaneously rather than sequentially. This makes them the go-to infrastructure for AI training, deep learning, scientific computing, and any application that requires crunching large datasets at speed.
GPU-accelerated servers power some of the most demanding applications across industries:
- AI & deep learning: train large language models (LLMs), computer vision models, and neural networks at scale.
- Scientific research & HPC: run complex simulations in genomics, climate modeling, fluid dynamics, and physics.
- Healthcare & medical imaging: accelerate medical image analysis, drug discovery pipelines, and diagnostic AI models.
- Video processing & rendering: handle 4K/8K transcoding, real-time 3D rendering, and visual effects pipelines.
- Data analytics & financial modeling: process massive datasets, run simulations, and power real-time fraud detection.
- Manufacturing & engineering: enable digital twin simulations, CAD/CAM acceleration, and predictive maintenance.
Learn more about what dedicated GPU servers are used for.
It depends on the model size. For fine-tuning smaller models, 4 GPUs may suffice. For training large language models (LLMs) or frontier AI models, 8-GPU HGX platforms with NVLink and InfiniBand interconnects are typically required.
Here are the key parameters to consider:
- Workload type: Training → high VRAM, NVLink/InfiniBand. Inference → lower GPU count, high throughput
- Number of GPUs: 4-GPU systems for mid-range HPC; 8-GPU HGX for frontier AI
- Cooling environment: Air cooling for standard racks; immersion/DLC for high-density or green data centers
- CPU architecture: Intel® Xeon® or AMD EPYC™
- Interconnect needs: InfiniBand for multi-node AI clusters; standard Ethernet for standalone deployments.
Yes. 2CRSi offers GPU servers with direct liquid cooling, single-phase immersion cooling, and dual-phase immersion cooling, alongside traditional air-cooled systems. These options help data centers achieve better energy efficiency and lower PUE scores.
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