A private GPU cloud
A shared compute environment on owned servers for teams that need dedicated capacity and control.
Sustainablemindz services
Cloud Setup and Migration
Dedicated capacity and cloud flexibility, with a deliberate operating model.
Illustrative infrastructure imagery
The scope
What's included
Private, hybrid or public: we design and set up the cloud environment for your AI workloads, and move you off hyperscaler spend where owning hardware is cheaper.
- Deployment
- Private, hybrid, public cloud or colocation
- Scheduling
- Kubernetes or Slurm, tenancy and quotas
- Connectivity
- Network boundaries and hybrid access
- Migration
- Dependencies, validation and rollback planning
- Operations
- Monitoring, access controls and ownership
Cloud and colocation options review
Compare environments against compute needs, data location, connectivity and operating responsibilities.
Private cloud build on your GPU servers
Turn dedicated hardware into a usable environment for your teams and AI workloads.
Hybrid connectivity
Plan the links and access between on-premises systems, colocation and public cloud services.
Migration planning and execution
Map dependencies, sequence the move and validate workloads in the target environment.
Cost modelling: cloud versus owned
Compare compute, storage, networking, facilities and operations using your expected utilisation.
Security and access setup
Configure agreed access controls, network boundaries and operational safeguards.
From requirement to delivery
How it works
- 01
Assess the current environment
Review workloads, dependencies, data governance, usage and cloud costs.
- 02
Choose the target design
Compare private, hybrid and public options with explicit cost and capacity assumptions.
- 03
Build and migrate
Prepare the environment and move workloads in agreed phases with a rollback plan.
- 04
Validate and transition
Test access and workload behaviour, then document the operating model and responsibilities.
Example scopes, not customer case studies
Typical projects
A hybrid AI deployment
Dedicated training or inference hardware connected to existing public cloud applications and data services.
A cloud-to-colocation migration
A phased move for predictable workloads where a cost model supports owning the hardware.
Documented delivery
What you receive
The final deliverables are defined in your agreed scope.
- Platform design: tenancy, networking, storage and access model
- Configured GPU scheduling (Kubernetes or Slurm) with quotas
- Monitoring dashboards for utilisation, temperature and job health
- Security baseline: identity, network segmentation and audit logging
- Operations runbook and admin training session
After handover
Support and assurance
Managed operations option
We can monitor and maintain the platform on an agreed schedule, or hand over fully to your team.
Capacity reviews
Periodic reviews of utilisation and queue times to plan when and how to expand.
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 · 5 min read
Buying vs renting GPUs: when each makes sense
Build your next AI environment.
Share your workload, location and timeline. Start with a configuration quote or an engineering conversation.