Managing AI: Secure, Optimize, and Maximize Your AI Initiatives
You Can’t Secure What You Don’t Control
Scroll Down to See More Resources ↓A private LLM deployed on VergeOS as a standard Kubernetes container, answering live questions, rescheduling onto a second GPU host after a node drops, and one API driving compute, storage, networking, and data protection.
Run private AI on hardware you own
The model in this session ran as a standard container on commodity x86 servers, with the prompts, the records, and the weights on the customer cluster. Book a technical session and we will walk the same deployment against your environment. Bring your GPU inventory and your current AI spend.
Schedule a DemoGet a design for your own environment
Model choice and GPU count drive the hardware conversation, and no two clusters size the same way. A technical design report maps your existing servers, your resiliency requirements, and your private AI target into one plan you can budget against.
Build Your Technical Design ReportGo deeper on private AI
Presentation: Private AI For Infrastructure and Organizations
The full argument as scrollable sections. What renting AI costs beyond the token bill, the three kinds of control you hand over, and how VergeOS runs the model on your own cluster.
View the Presentation → DatasheetPrivate AI For Infrastructure and Organizations
Private AI as a workload on VergeOS. Container support, one unified API across the platform, no lock to a single GPU vendor, and what happens to token cost when the model runs on hardware you own.
Read the Datasheet →