⚡️ GPU Cloud and Resource Shortage: How Businesses Compete for AI Computing Power

The Russian cloud services market grew by 32.8% in 2025 to 416.5 billion rubles — and the main driver was the GPU segment. In 2026, demand for GPUaaS is expected to grow by over 50%, and 50–60% of new IT projects are already oriented toward GPU servers for AI and ML. But behind this boom lie systemic constraints.

⛔️ Problem: Shortage + Sanctions
Access to modern accelerators is limited for Russia — US export controls are tightening, parallel imports are shrinking. GPUs are literally being swept off the market: rental prices for startups have risen by 32%, and queues for capacity are growing. Globally, NVIDIA captured 94% of the discrete accelerator market (as of Q4 2025), AMD fell to 5%, Intel to 1%.

🆗 Market Response: Virtualization and Diversification
Providers optimize utilization through multi-tenancy and fine-tuning on others' clusters. Pay-as-you-go platforms offer 15–35% savings compared to investing in own GPU assets. Globally, a trend is forming away from NVIDIA monopoly → AMD Instinct MI355X, Intel Gaudi, custom TPUs/NPUs.

💡 Below is a simplified total cost of ownership calculation:
Own server 2×A100 ≈ 1.2 million rubles + ~175,000 rubles/year for maintenance. Cloud — ~25,000 rubles/month. At GPU utilization <50%, renting is more profitable. At >70% load, own hardware pays off in 12–18 months.

Have you calculated the TCO of GPU cloud vs. own hardware for your AI tasks? Which turned out more profitable — renting, buying, or hybrid? Share in the comments 👇

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