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A CPU (Central Processing Unit) is like a worker that handles many different tasks, but usually one at a time. A GPU, on the other hand, is like having a whole team working in parallel, which makes it perfectly suited for the heavy calculations needed in AI and data analysis.

GPUs are also used for scientific research, engineering simulations, 3D rendering, video processing, data analytics, and other high-performance computing (HPC) tasks. Their ability to perform many calculations simultaneously makes them useful wherever large amounts of data need to be processed quickly.

NVIDIA Blackwell B300 GPUs are part of the Blackwell Ultra generation and are designed for large-scale AI training, inference, and reasoning. Compared with the original Blackwell generation, they offer up to 288 GB of HBM3e memory per GPU, 50% more NVFP4 computing performance, and up to twice the attention-layer performance. This makes them particularly suitable for large models, long context windows, high-volume inference, and workloads serving many users simultaneously.

The AI factory draws its primary power from its own solar installation. For reliability, DRUPS technology stabilizes the incoming power and immediately bridges grid interruptions. This layered system is designed to protect GPU workloads from both power fluctuations and extended outages.

Yes. Depending on availability and project requirements, startups and smaller teams can access the same underlying infrastructure through setup matched to their workloads. This gives them enterprise-grade computing capacity without requiring them to build and operate their own GPU cluster.

NVIDIA B300 infrastructure is most valuable for industries running large, compute-intensive AI workloads. It is particularly well suited to training and fine-tuning large models, AI reasoning, high-volume inference, simulations, and large-scale data processing. Ultimately, the benefit depends less on the industry itself and more on the scale and complexity of its computing needs.

NVIDIA Blackwell B300 GPU capacity is available through specialized AI infrastructure and cloud providers. In the South Caucasus, Eleveight AI operates B300 infrastructure in Armenia. Access depends on the workload, required scale, deployment model, and current capacity. Organizations can contact the Eleveight AI team to discuss their technical requirements and available options.

Our aim is infrastructure that mostly stays out of your way. It is built to NVIDIA Reference Architecture, so your jobs run as you wrote them, with clean setup and few surprises. When you do want a person, our Infrastructure Co-pilot means you reach real infrastructure engineers who know your setup, not a ticket queue.

No. Eleveight AI’s AI Factory does not consume water for cooling, and it never will, in any phase. Today the AI factory uses free/air cooling, which its high-altitude location makes possible for most of the year. As the factory scales, where air alone cannot handle the density, it moves to closed-loop liquid cooling with a dielectric coolant. Both approaches keep Water Usage Effectiveness (WUE) at 0 L/kWh. The cooling method changes as we grow, but the zero-water commitment does not.

During a grid interruption, the AI Factory uses DRUPS (a diesel rotary UPS) technology to keep systems running until backup generators take over. Instead of chemical batteries, DRUPS stores energy mechanically in a rotating system. This lowers fire risk and reduces the environmental impact associated with producing, replacing, and disposing of large battery banks.