Managing AI: Secure, Optimize, and Maximize Your AI Initiatives
You cannot secure what you do not control. Watch a private LLM deployed on VergeOS, running as a standard Kubernetes container on hardware you own, answering real questions with the prompts and the weights still inside the building.
Renting AI Costs More Than Money
Most organizations start their AI work in the public cloud. Prompts, records, and models leave the building, and the meter runs by the token. That path starts fast and turns expensive, and the expense is the smaller problem. The larger one is control. When another company owns the hardware and sets the terms, you cannot secure, optimize, or maximize what runs on it.
This session takes the other path. VergeOS runs private AI on infrastructure you already own. The model you choose runs as a standard container on your own cluster, and the same platform that hosts it also runs compute, storage, networking, and data protection on one code base.
What David Demonstrates
A Private LLM on Your Own Cluster
Stand up a model you select on hardware you own. The prompts, the records, and the weights stay behind your firewall.
AI as a Standard Kubernetes Container
Docker and Kubernetes support ships in VergeOS today. The model runs as a normal container rather than inside a separate bolted-on stack.
What Happens When a GPU Node Drops
The workload reschedules onto another GPU host and the inference stack restarts there. Shared storage is the prerequisite, either an NFS share or a Kubernetes persistent volume claim.
One API Across the Whole Platform
Compute, storage, networking, and data protection sit behind a single API, with Verge CLI driving all of it from one command line. Four toolchains collapse into one.
A Model That Learns Your Infrastructure
Verge MCP points a model at that same interface. The model learns the state and the shape of the platform, and your admin decides and acts on what it surfaces.
What Happens to the Token Meter
Running the model on owned hardware removes the per-token charge and the egress fee. An open-ended operating line converts into a capital number you set.
Built for Teams Facing a VMware Renewal and an AI Mandate
Infrastructure directors and virtualization admins running VMware will recognize the platform argument first. CIOs and CTOs asked to deliver AI to the business this year, without an open-ended cloud bill and without shipping proprietary data to a provider, get the cost and sovereignty case. Systems engineers, resellers, and MSPs who validate claims before a buyer acts will want the demo itself.
The Private Cloud Operating System
VergeOS is one operating system on a single code base that converges virtualization, storage, networking, and data protection. No controller VM reserves cores and memory on every node before a customer workload runs, and the software runs on commodity x86 across mixed hardware generations. On an AI project competing for GPU-adjacent resources, that reclaimed capacity is a number you can put in a spreadsheet.
Local models run on standard hardware, and the platform does not tie you to a single GPU vendor. Nothing in this session is a new product announcement. Container support, Verge CLI, and Verge MCP all ship today. What the demo shows is the combination.
Delivered at the ActualTech MegaCast
VergeIO presented this sponsored session at the ActualTech MegaCast on July 30, 2026. George Crump opens with the three-part argument, covering the VMware exit onto one code base, using AI to make the IT job easier, and providing AI as a service to the organization. David Vincent then runs the live demo against a working cluster. The recording carries both parts.
Watch the Recording
Submit your business email for instant access to the full session, including the private LLM deployment, the failover sequence, and the unified API walk-through.