Some workloads just don’t fit neatly onto a traditional VPS. The moment you start training neural networks, running real-time rendering pipelines, or hosting GPU-accelerated simulations, a CPU-only server becomes a bottleneck. A GPU VPS is built specifically to handle that kind of work — and Cloudzy’s offering is worth understanding in detail before you commit.
The Hardware
Cloudzy currently offers three GPU options: the NVIDIA RTX 5090, the A100, and the RTX 4090. Each targets a slightly different type of workload, so picking the right one matters. The RTX series is generally better suited for rendering and gaming use cases, while the A100 is the go-to for large-scale AI training and inference where raw memory bandwidth and stability under sustained load are priorities.
Regardless of which GPU you choose, all plans are built on DDR5 memory, fast vCPUs, NVMe SSD storage, and up to 40 Gbps network connectivity — so the GPU won’t be the only thing pulling its weight.
Standard inclusions across all plans: full root access, free DDoS protection, 99.99% network uptime, 24/7 support, and coverage across 10+ data center locations worldwide, including Frankfurt, Amsterdam, and New York.
Who Actually Uses This?
AI and machine learning teams are probably the most natural fit. Whether you’re running inference on an open-source LLM, fine-tuning a model on proprietary data, or training from scratch, having a dedicated GPU with full CUDA access — and no queue times — makes a real difference in iteration speed.
3D artists and studios benefit from the raw VRAM. The RTX 4090, for instance, carries 24 GB of GDDR6X memory, which meaningfully changes what’s possible for real-time previews and complex scene rendering without constantly offloading to RAM.
Cloud gamers and game developers can stream high-resolution, high-framerate gameplay from Cloudzy’s network without needing local GPU hardware. Low-latency locations like Frankfurt and New York help keep the experience responsive.
HPC and data science teams doing computation-heavy work — simulations, genomics pipelines, financial modeling — can deploy any stack they need thanks to full root access, then take advantage of GPU parallelism to slash runtimes.
Startups and solo developers who need GPU compute but can’t justify a hardware purchase will find hourly billing particularly useful. Spin up when you need it, tear it down when you don’t.
Availability and Delivery
GPU hardware isn’t infinitely available, so delivery typically takes up to 14 days. If a specific configuration isn’t currently listed as available, it’s worth contacting the support team directly — they can check whether a pre-order is an option for your setup.
Browse available plans at “GPU VPS” or open a support ticket!