Data Centers

Built for AI from the ground up.

Pruna Trading designs and operates the data centers behind every GPU hour we sell — with the power, cooling and networking that high-density NVIDIA compute demands.
Illustration of a GPU data hall
Inside a Pruna Trading data hall
01

Five things every facility gets right

01

Power

High-density power delivery sized for full racks of GPUs, with redundancy built into the design.
02

Cooling

Liquid cooling that removes heat at the source, so GPUs hold their performance under sustained load.
03

Networking

A high-bandwidth, low-latency fabric connecting GPUs across nodes for distributed training.
04

Security

Controlled physical access, surveillance and strict separation between customer environments.
05

Operations

Round-the-clock monitoring and on-site teams keeping hardware healthy and capacity available.
02

Cooling that keeps GPUs at full speed

Modern GPUs produce more heat than air alone can handle efficiently. Our liquid-cooled racks carry heat away directly, keeping clocks high and hardware reliable through weeks-long training runs.

  • Direct liquid cooling for GPU racks
  • Stable thermals under sustained load
  • Designed for energy efficiency
Illustration of a liquid cooling loop
03

A fabric built for multi-node AI

Distributed training is only as fast as the network between GPUs. Our data centers are wired with high-bandwidth, low-latency networking so clusters behave like one machine.

  • High-bandwidth GPU-to-GPU networking
  • Low-latency interconnect between nodes
  • Scales from one node to full clusters
Illustration of a network fabric topology
04

Racks engineered for density

GPU servers are heavier, hungrier and hotter than traditional IT. Every rack position is planned around their power, cooling and cabling needs from day one.

  • Purpose-built rack layouts
  • Structured power and network cabling
  • Capacity planned for future GPU generations
Illustration of a GPU server rack

Capacity is coming online. Reserve yours.

Talk to us about on-demand GPU hours, reserved capacity or a dedicated cluster in our data centers.