Accelerate AI development and deployment
Use GPU infrastructure for training, fine-tuning, inference and private AI.
What changes
Teams need accelerator capacity close to their data, with isolation for sensitive models and datasets.
Training, fine-tuning and inference run on in-region GPU infrastructure with data that stays local.
Recommended approach, at a logical level
Steps in order. Each maps to KonetCloud services below; detailed designs are produced with your team.
- 01Stage datasets in object and block storage
- 02Provision GPU compute for training and fine-tuning
- 03Serve models on inference infrastructure behind load balancing
- 04Isolate sensitive workloads on private AI infrastructure
KonetCloud services involved
AI and GPU
High-performance GPU infrastructure for model training, fine-tuning, inference and accelerated computing.
Storage
Scalable storage services for applications, databases, backups, archives and enterprise data.
Networking
Build isolated networks, connect applications, distribute traffic and securely connect cloud and enterprise environments.
Compute
Flexible virtual compute for websites, enterprise applications, databases and demanding production workloads.
Migration, implementation and operations
Benefits are stated only where evidence exists; we would rather show you than tell you.
- Capacity planning and requests for accelerator quotas
- Data locality and isolation requirements
- Managed AI platform services are reserved for a later release
Get started
Talk through the architecture.
Bring your constraints. An engineer will map them to zones, services and an operating model — then you decide.