Dedicated GPU servers
Dedicated GPU servers matched to your workload. Platform, configuration, location, timing and service scope are set out in the proposal.
- Eight-GPU NVIDIA HGX servers
- Single-tenant, not shared
- Sized to the workload
Dedicated GPU capacity.
Dedicated GPU servers and clusters for AI companies and cloud operators. Reserved capacity is available only through long-term contracts.
Servers or a whole cluster.
Training, inference or both: size the capacity to what the workload has to do.
Dedicated clusters
A complete environment: GPU configuration, interconnect, storage and operating requirements all belong in the brief.
- Servers or rack-scale systems
- Fabric, storage and access planned together
- Platform and location set per project
Plan a long-term commitment.
Reserved capacity is available only under long-term contracts. Share your preferred start date and intended commitment, so that capacity and commercial terms can be assessed together.
Discuss reserved capacityDescribe the workload.
Share the workload, preferred GPU platform, location and timing, where known.
- Workload and preferred GPU platform
- Server or cluster requirement
- Network and storage requirements
- Preferred location and start date
- Intended duration and reservation needs
- Software, access and support requirements
Questions about GPU capacity.
Can we request a dedicated cluster?
Yes. Include the intended platform, scale, connectivity, storage and operating requirements in your project brief.
How is reserved capacity arranged?
Reserved capacity is available only through long-term contracts. Capacity, timing, duration and commercial scope are agreed for the requirement.
Do we need to specify the entire environment?
Start with your workload and the requirements you know. Include any platform preferences and the decisions that need further discussion.
Build your own environment.
If acquiring systems and arranging colocation better fits your plans, those requirements can be discussed alongside dedicated capacity.