A research training cluster
Multi-node GPU compute with high-speed interconnects, shared storage and job scheduling for research teams.
Sustainablemindz services
AI Infrastructure
A complete compute environment, from architecture to operational handover.
Illustrative infrastructure imagery
The scope
What's included
We plan and deliver the full stack for AI workloads: GPU compute, high-speed networking, storage, power and cooling, through to the software layer your engineers use.
- Compute
- GPU memory, node count and workload sizing
- Network fabric
- InfiniBand or Ethernet, topology and bandwidth
- Storage
- Dataset throughput, checkpoints and capacity
- Facilities
- Rack density, power budget and cooling readiness
- Platform
- CUDA, Kubernetes or Slurm and monitoring
Cluster architecture and sizing
Match GPU capacity, memory and node count to training, fine-tuning and inference workloads, with room to grow.
InfiniBand and Ethernet fabric design
Plan interconnects, switching and topology around the communication demands of your cluster.
High-performance storage
Design data and checkpoint storage to keep pace with compute, with capacity and throughput considered together.
Rack, power and cooling planning
Review rack density, electrical capacity and cooling requirements before equipment reaches your site.
OS, drivers, CUDA, Kubernetes and Slurm setup
Configure a compatible software stack and the orchestration or scheduling tools your team needs.
Monitoring and handover
Set up operational visibility, validate the agreed configuration and hand over documentation to your team.
From requirement to delivery
How it works
- 01
Understand the workload
Review models, datasets, performance needs, budget and the facilities available.
- 02
Design the full stack
Define compute, fabric, storage and site requirements as one architecture.
- 03
Build and configure
Coordinate equipment deployment and configure the agreed software environment.
- 04
Validate and hand over
Check the system against agreed acceptance criteria and prepare your team to operate it.
Example scopes, not customer case studies
Typical projects
An enterprise inference environment
Dedicated GPU capacity for model serving, with access controls and monitoring suited to internal applications.
Expansion of an existing GPU estate
Additional compute and a review of fabric, storage, power and cooling bottlenecks before scaling.
Documented delivery
What you receive
The final deliverables are defined in your agreed scope.
- Architecture document covering compute, network fabric, storage and management
- Rack elevation and power budget per rack
- Cabling and port maps for compute and storage networks
- Acceptance test report (GPU burn-in, NCCL bandwidth, storage throughput)
- Handover pack with credentials, runbooks and escalation contacts
After handover
Support and assurance
Manufacturer warranty
Hardware is covered by the OEM's standard warranty. Extended warranty and on-site response can be quoted with your order.
Post-deployment support
Remote support for drivers, firmware, scheduler and fabric issues after go-live, with optional support retainers.
Export and compliance
We handle commercial invoices, packing lists and end-user documentation, and check every order against applicable export regulations before it ships.
Before you start
Frequently asked questions
Buyer guide · 6 min read
How to choose between HGX B300, B200, H200 and H100
Build your next AI environment.
Share your workload, location and timeline. Start with a configuration quote or an engineering conversation.