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GPU server configurator
Configure a GPU server
Komponenty serwera
number of cores
Bundle
RAM
Period
Case
Network cards
Graphics card
Enter quantity:
Hard drives
Hard drives
The selected disks require a controller
IP Addressing
Enter quantity:
Bandwidth of the link
Operating Systems
AlmaLinux
Microsoft Windows
Ubuntu
Linux Debian
Gentoo Linux
Alpine Linux
Proxmox
VMware ESXi
Other software
Enter quantity:
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GPU server and accelerated computing
Configure a GPU server for AI, data processing and accelerated workloads
The GPU server configurator helps select resources for AI projects, machine learning, rendering, data analysis, model testing and applications that need hardware acceleration.
Typical use cases
- GPU server for AI, machine learning and inference tests
- CUDA-ready environment for rendering and data analysis
- dedicated accelerated server for demanding applications
- research, development and production workloads with high compute load
What you can select
- GPU, CPU, RAM and storage selected for workload type
- Linux or Windows Server and optional software
- backup, network, IP addressing and administration options
- integration with dedicated servers, Cloud Pro and colocation
Related services and tools
When should I choose a GPU server?
A GPU server is useful when the project needs hardware acceleration, parallel computing, data processing, AI, rendering or model testing.
Is GPU server suitable for AI and machine learning?
Yes. A GPU environment can be used for AI experiments, machine learning, inference, compute tests and CUDA-like workloads.
Can DataHouse administer a GPU server?
Yes. GPU servers can be combined with administration, monitoring, backup and technical support.
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