“L’adiabatique et le refroidissement liquide indispensables pour les fermes GPU.” In the age of AI-scale compute, adiabatic and liquid cooling have become the only credible path to safe, efficient, and scalable GPU farms. When you train large models or run HPC workloads, heat density and power transients overwhelm legacy air-only designs. In this guide, we explain when and how to use adiabatic systems and liquid cooling, why hybrid architectures outperform single-mode cooling

GPU/TPU/LPU: understand the differences and use cases. This no‑nonsense guide clarifies what each accelerator does best, when to choose one over the others, and how to align your AI stack with performance, latency, and sustainability goals.   In brief GPUs are general‑purpose accelerators with the richest ecosystem—great for training and versatile inference. TPUs specialize in tensor math for deep learning—excellent for large‑scale training and batched inference on Google Clo

Vision and innovation at the intersection of energy and computing power. At Score Group, we turn this convergence into measurable performance, resilience, and sustainability—aligning your energy footprint with the computing capacity your business needs today and tomorrow. “Where efficiency meets innovation.” As an integrator, Score Group brings together three pillars—Energy, Digital, and New Tech—through our Noor divisions to design, deploy, and operate end‑to‑end solutions t

Smart buildings & energy efficiency: practical ways intelligent buildings cut energy use in 2025 while boosting comfort and ROI. From...