Workload-led design
Start with your model, data and target performance. Shape the compute architecture around training throughput, inference latency and deployment requirements.
Future Watt / AI Infrastructure
From energy.
To AI compute.
Power infrastructure for the next generation of AI data centers.
AI data center visualization
Future Watt Cloud
Managed AI
Plan model training, fine-tuning and production inference around your workload, compute requirements and deployment environment.
From requirements to deployment
Build the right foundation
for your AI.
Connect workload decisions with the network, operating environment and infrastructure behind them.
Cluster networking
Plan the fabric, storage access and data movement together. Evaluate interconnect requirements before expanding a distributed workload.
Power and site integration
Coordinate compute requirements with the energy, storage and data center planning that supports a sustainable deployment.
Environment control
Define tenant isolation, access permissions and private connectivity around your data and operating requirements.
Operational visibility
Agree the utilization, performance and cost metrics that matter to your team, and use them to guide capacity decisions.
A clear support plan
Set responsibilities, escalation paths and service expectations with the infrastructure team before deployment.
AI applications
Different workloads.
A focused compute plan.
Model training & fine-tuning
Build a deployment plan for language, vision and multimodal models, from initial experiments to larger training runs.
Production inference
Balance response times, model memory and request volume for reasoning, search and AI applications.
Deep learning
Explore compute for computer vision, scientific research and analysis of large data sets.
Recommendation systems
Plan recurring model updates and serving capacity for personalized experiences.
Predictive maintenance
Use machine learning to identify unusual patterns in equipment and operational data.
Digital twins
Evaluate simulation workloads for energy networks, industrial processes and complex physical systems.
Your questions, answered
Plan with clarity.
Tell us what you are building. We will help turn the requirements into a practical infrastructure discussion.
Speak with FutureWatt ↗Which Managed AI workloads can I discuss?
Discuss model training, fine-tuning and production inference. Share your model, target performance and deployment requirements so our team can scope a proposal.
Can I plan for both training and inference?
Yes. Share your model size, data volume, throughput and latency targets so the team can scope a configuration.
How are storage and networking specified?
They are sized with the workload, including data ingestion, checkpointing and distributed compute requirements.
What about sensitive data?
Agree access controls, isolation, connectivity and compliance requirements before deployment.
Can I bring my own software?
Discuss your frameworks, containers and operating environment during technical scoping.
How do I obtain pricing and support terms?
Contact FutureWatt with your capacity and timeline. Pricing, availability and service terms are confirmed in the proposal.
Start the conversation
What will you build next?
Share your workload, preferred GPU architecture, required capacity and deployment timeline.
Plan your AI infrastructure