GPU & AI Servers

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.