01 / AI TEAMS
Foundation model training
Distributed compute; accelerators; gradient synchronisation; data pipelines; checkpoint resilience.
ENTERPRISE APPLICATIONS
Different workloads. Different infrastructure decisions.
Illustrative use cases across industry and research. Enterprise Cloud GPU does not provide these services or claim these organisations as customers.
02 / APPLICATION TEAMS
Production inference
Serving latency, throughput, autoscaling, model residency, observability and data controls.
03 / FINANCIAL SERVICES
Risk, modelling & research
High-performance simulation, workload isolation and clear governance for sensitive data.
04 / LIFE SCIENCES
Scientific discovery
Large datasets, accelerated simulation, reproducibility and secure collaboration.
05 / MANUFACTURING
Engineering & digital twins
Computational design, simulation pipelines, performance-sensitive storage and visualisation.
06 / ENERGY & UTILITIES
Forecasting & optimisation
Operational datasets, model testing, data sovereignty and infrastructure energy trade-offs.
A PRACTICAL FRAMEWORK
Ask five questions before committing
Compute selection should begin with the application objective and operational constraints — not a model name alone.
- Which workload and success metric will the infrastructure support?
- How much GPU memory, performance and network bandwidth are actually required?
- How predictable is utilisation, and which contract model matches it?
- What data, security, residency and service assurances are necessary?
- How will total operating cost and energy use be assessed?
EXPLORE THE POSSIBILITY
Start with a clearer compute brief.
Compare planning assumptions, understand your infrastructure choices, and prepare a structured enquiry.