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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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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.
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.
Optimize Token Economics in Real Time
Track token throughput, latency, and cost by model, tenant, and workload. Compare private inference economics with public APIs to make smarter placement
decision
Operate a Multi-Tenant AI Service Layer
Manage tenant onboarding, GPU allocation, API access, RBAC, and usage metering from a unified control plane. Align workload placement with SLA, cost, and available capacity.
Orchestrate LLMs Across Runtimes and Clouds
Abstract inference across vLLM, LiteLLM, TensorRT-LLM, and public APIs. Port workloads between private and public environments while supporting 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 while securing workloads with hardware-backed confidential computing.
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.

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

Deliver & Manage AI Factory Faster
with UPC Vektor

Read The Solution Brief

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, PUE, and interconnect economics.
02
Make AI Revenue Measurable
Meter token consumption and workload costs by tenant to support transparent chargeback, showback, and pricing.
03
Keep Workloads Portable
Orchestrate across private and public LLM environments without vendor lock-in, giving you flexibility as models and providers evolve.
04
Scale Without Losing Control
Enforce tenant-level policies, maintain auditability, and protect workloads with confidential computing as your AI service expands.
05
Accelerate Production Readiness
Move from factory design to operational handover through a structured implementation path built for repeatable 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 for your AI factory.

02

Build
Deploy the infrastructure 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 across production.

04

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

Get started

Turn GPU Capacity into AI Revenue

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