Leader in ISG Private Cloud Report 2026.

UnitedLayerBlue Version

Vektor™

Power, Run, and Manage Your AI Factory

Optimize GPU performance, reduce cost-to-serve, and streamline AI training and inference with centralized infrastructure management.
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Read the Solution Brief
Tokens
ThroughputLatencyCost per million tokensPrivate vs public inference
Tenants
OnboardingGPU allocationAPI accessUsage meteringChargeback & showback
LLM runtimes
vLLMLiteLLMTensorRT-LLMPublic APIsPyTorch, TensorFlow, ONNX, CUDA
Workloads
TrainingInferencePlacement by SLA, cost, and capacity
Fabric
Rack topologyNetwork topologyInterconnect economics
Silicon
GPU clustersCapacity poolsThermalPower & PUE
GovernanceRBAC, SSO, MFAPer-tenant policyAudit logsConfidential computing
ChatOpsFleet healthIdle GPU detectionRunbooks & rollbacksHuman approval

One control plane from the GPU up to the token, with governance and conversational operations across every layer.

The platform

Full-Stack AI Operations, from Silicon to Token

UPC VEKTOR orchestrates GPU infrastructure, fabric, workloads, tenants, and token consumption through a unified operational layer built for scalable AI services.

One console for the whole AI factory

Spot idle capacity, approve the fix, and watch utilization and cost-to-serve improve, without
leaving UPC Vektor.

Higher GPU
utilization

Find idle GPUs across racks and put them back to work on the workloads that need them.

Lower cost-to
-serve

See the cost per million tokens drop as capacity moves to where demand is.

Human-approved automation

Vektor proposes the change, your team approves it, and the runbook does the rest.

One view of everything

Fleet, token economics, tenants, and ChatOps side by side in a single console.

Optimize your GPU fleet at scale

Provision and monitor clusters, map rack and network topology, track thermal and power
utilization, and manage capacity across GPU pools based on workload demand.

Cluster
provisioning

Stand up and monitor GPU clusters from one
place.

Topology
mapping

See rack and network topology, down to the top-of-rack switch and spine fabric.

Thermal and power tracking

Catch hotspots early and track power draw and PUE across the fleet.

Demand-based capacity

Rebalance training, inference, and free pools as workload demand shifts.

Optimize token economics in real time

Track token throughput, latency, and cost by model, tenant, and workload, and compare private inference economics with public APIs to make smarter placement decisions.

Live inference
metrics

Throughput, p95 latency, and cost per million tokens for every model and tenant.

Private vs public comparison

Compare what each workload costs on your GPUs against public APIs.

Smarter
placement

Get recommendations on where each workload runs most cost-effectively.

True cost per
token

Placement factors in GPU power, PUE, and interconnect economics.

Operate a multi-tenant AI service layer

Manage tenant onboarding, GPU allocation, API access, RBAC, and usage metering from a unified control plane, with placement aligned to SLA, cost, and available capacity.

Fast tenant onboarding

Add a tenant, allocate GPUs, issue API keys, and assign roles in one flow.

Access
control

Per-tenant API access with role-based permissions for service management.

Usage
metering

Meter token consumption and workload cost by tenant for chargeback and showback.

SLA-aware
placement

Workloads land where SLA, cost, and available capacity line up.

Orchestrate LLMs across runtimes and clouds

Abstract inference across vLLM, LiteLLM, TensorRT-LLM, and public APIs, and port workloads
between private and public environments.

One endpoint

An OpenAI-compatible API router deployed in front of every runtime.

Multiple runtimes

Serve with vLLM, LiteLLM, TensorRT-LLM, or public APIs.

No vendor lock-in

Move workloads between cloud environments without application changes.

Framework support

Works with PyTorch, TensorFlow, ONNX, and CUDA.

Govern AI operations with control and compliance

Enforce RBAC, SSO, MFA, and per-tenant policies, centralize audit logs and compliance visibility, and secure
workloads with hardware-backed confidential computing.

Identity and
access

RBAC, SSO, and MFA enforced on every request for secure access control.

Per-tenant
policy

Per-tenant policies prevent cross-tenant access, ensuring complete isolation of data

Central audit
trail

Every allowed and denied action is logged in one place for compliance.

Confidential computing

Hardware-backed protection keeps workloads encrypted while in use.

Manage the factory through ChatOps

Monitor fleet health, identify idle GPUs and performance issues, and execute runbooks,
deployments, and rollbacks through conversational operations with human approval.

Natural Language Interface

Check fleet health or identify idle GPUs instantly using natural language queries.

Detect Issues
Faster

Surface idle capacity and performance problems before users notice.

Runbooks on
Demand

Run deployments, reassignments, and rollbacks from the conversation.

Human in the
Loop

Nothing changes until someone on your team approves it.

Sovereignty & private inference

Private AI Inference, Within Your Boundary

Keep models, data, and inference workloads under your control while meeting residency and regulatory requirements and optimizing the economics of every inference.
  • Run LLMs on private GPUs
  • Control data residency and compliance
  • Protect workloads with confidential computing
  • Compare private and public inference costs
Upc Vektor Sovereignty Private Inference 1

Why UPC VEKTOR

Turn AI Infrastructure into Measurable ROI

Optimize GPU utilization, power, cooling, and workload placement while gaining control over token economics, isolation, and portability across private and public inference environments.

01

Maximize Fleet Utilization

Optimize workload placement using real-time cost per million tokens, factoring in GPU power and PUE.

02

Make AI Revenue Measurable

Meter token consumption and workload costs by tenant to support transparent pricing.

03

Keep Workloads Portable

Orchestrate across private and public LLM environments without vendor lock-in, as models evolve.

04

Scale Without Losing Control

Enforce tenant-level policies, and protect workloads with confidential computing.

05

Accelerate Production

Move from factory design to operational handover through a structured path built for deployment.

Delivery model

From Infrastructure to Production-Ready AI

UPC VEKTOR structures AI factory operations across four delivery stages, from infrastructure design to operational handover, giving clients a production-ready factory with full autonomy post-delivery.

01

Design

Plan GPU topology, tenant architecture, network fabric, service catalog, capacity, and token pricing.

02

Build

Deploy the stack, onboard tenants, activate workloads, connect LLM APIs, and validate token flows.

03

Operate

Monitor GPU health and token throughput, optimize workloads, manage costs, and enforce governance.

04

Monetize

Meter tenant usage, automate chargeback and showback, and measure revenue against cost-to-serve.

Turn GPU Capacity into AI Revenue

Experience global scalability and unmatched reliability through our hyperscaler partnerships.

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