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Typical Use Cases for GPU
I continue to explore GPU computing, and this article discusses typical use cases for GPUs. Large companies, such as Wildberries, build their own specialized data centers for AI. Medium-sized companies purchase GPU servers for their racks. Renting in the cloud may be suitable for me.
The first step in mastering the topic of GPU - computing "Glossary of Terms for Beginners in GPU Computing (2026)" is available at the link.
I continue to delve into GPU (AI) computing, in this article with typical use cases for GPUs. Large companies, such as Wildberries, are building their own specialized data centers for AI. Medium-sized companies are purchasing servers with GPUs for their racks.
For others, cloud rental might be suitable, for example:
A researcher fine-tuning an LL on a specialized chassis
A startup founder launching an AI service without a budget for a data center
A freelancer who needs a full day for video rendering once a month
An AI developer testing the architecture before scaling (my story).
7 examples of "renting vs buying" in 2026:
Scenario | Requirements | Renting vs Buying Economics |
|---|---|---|
Model Fine-tuning (7–13B parameters) | 24+ GB VRAM, 1–2 days of operation | Renting 2×RTX 4090 for 48 hours ≈ ₽10,000. Buying two cards — from ₽500,000 + operating costs. |
Content Generation (images, videos) | 12–16 GB VRAM, batch processing | Freelancer: 4 hours of rental for ₽500 instead of recovering the cost of the card after 100+ orders. |
Inference of Large Models (70B+) | 40+ GB VRAM through quantization | Startup pays ₽300/hour instead of $20/hour on foreign platforms + avoids data export risks. |
Scientific Computing | 24/7 stability, access to datasets | Lab: ₽50,000 for a week of rental against ₽3,000,000 for equipment with 11 months of downtime per year. |
Video Rendering | 16+ GB VRAM, RTX acceleration | Videographer: ₽3,000 for 24 hours of rental instead of having their own card idle for 27 days a month. |
RLHF and Model Evaluation | Parallel launch of multiple instances | Researcher: ₽15,000 for 50 experiments instead of having the card idle between iterations. |
Production Deployment | High availability, auto-scaling | Startup: ₽150,000/month for reservation instead of ₽2,000,000 capital costs + sysadmin salary. |
💡 Key Insight: If my task requires less than 80–100 hours of power per month — renting is almost always more cost-effective than buying. The exception is a constant 24/7 load on a single configuration.
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