AI workloads are growing at a breakneck pace. Training transformer models, rendering complex visual effects, and simulating intricate biological systems now require compute power that far exceeds what traditional CPUs can offer. According to Gartner, over 80% of enterprise AI initiatives will rely on accelerated computing by 2026.

This guide is designed to help CTOs, ML engineers, data scientists, and infrastructure architects understand the full picture: how cloud GPU infrastructure works, what options exist in the market, how to optimize for performance and cost, and how to future-proof your deployments. At SpringDB, we’ve architected GPU workloads across sectors—from AI-native startups to Fortune 500 enterprises.