20–200 users. Centralized AI server. Software pods.
AI Center Server
┌────────┐ ┌────────┐ ┌────────┐ ┌────────┐
│ A6000 │ │ A6000 │ │ A6000 │ │ A6000 │
│ 48 GB │ │ 48 GB │ │ 48 GB │ │ 48 GB │
└────────┘ └────────┘ └────────┘ └────────┘
Unified GPU Pool: 192 GB VRAM
SteraOS AI Center
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Model │ │ Inference │ │ Pod │
│ Depot │ │ Engine │ │ Roster │
└───────────┘ └───────────┘ └───────────┘
┌───────────┐ ┌───────────┐ ┌───────────┐
│ Joules │ │ Training │ │ RBAC │
│ Economy │ │ Engine │ │ + SSO │
└───────────┘ └───────────┘ └───────────┘
│
┌──────────────┼──────────────┐
│ │ │
┌──────┴───┐ ┌──────┴───┐ ┌─────┴────┐
│ Terminal │ │ Terminal │ │ Mobile │
│ (desk) │ │ (desk) │ │ (field) │
│ €99-149 │ │ €99-149 │ │ pad │
└──────────┘ └──────────┘ └──────────┘
│ │ │
┌──────┴───┐ ┌──────┴───┐ ┌─────┴────┐
│ Finance │ │ Legal │ │ Support │
│ Dept. │ │ Dept. │ │ Dept. │
└──────────┘ └──────────┘ └──────────┘
In the sole & small tier, each machine has its own GPU and runs its own models. This works for individuals and small teams. But at 20+ users, a different architecture becomes more efficient: one powerful centralized server with multiple high-end GPUs, running all AI workloads for the organization.
The pod becomes software. Instead of buying a hardware box for each AI role, you deploy a software pod — a model configuration with an occupation definition — onto the central server. A financial analyst pod, a legal researcher pod, a customer support pod. Each runs on the shared GPU pool. Adding a new AI capability means deploying a new pod, not purchasing new hardware.
Workers access the AI Center through thin terminals — silent, fanless devices at each desk that cost a fraction of a full machine. They have no GPU of their own. All computation happens on the server. The terminal is just an interface. Field workers use Stera Mobile pads. Remote workers connect through the web. The intelligence is centralized. The access is everywhere.
Pod Roster
┌─────────────┐ ┌─────────────┐
│ Developer │ │ Analyst │
│ Pod │ │ Pod │
│ │ │ │
│ Model: 34B │ │ Model: 14B │
│ VRAM: 22 GB │ │ VRAM: 10 GB │
│ Dept: Eng. │ │ Dept: Fin. │
└─────────────┘ └─────────────┘
┌─────────────┐ ┌─────────────┐
│ Support │ │ Researcher │
│ Pod │ │ Pod │
│ │ │ Pod │
│ Model: 8B │ │ Model: 72B │
│ VRAM: 6 GB │ │ VRAM: 48 GB │
│ Dept: Supp. │ │ Dept: R&D │
└─────────────┘ └─────────────┘
Total VRAM allocated: 86 / 192 GB
Pods active: 4
Available for new pods: 106 GB
A software pod is a self-contained AI worker: a model, an occupation configuration, and a VRAM budget. It is deployed to the AI Center like a new hire is onboarded to a team. It has a role, a department, and a scope of responsibility.
Pods are co-developed with your organization. We work with your teams to understand the work, select or fine-tune the right model, define the occupation, and calibrate the pod for your specific operations. This is not off-the-shelf software. It is a purpose-built AI worker trained on your domain.
Joules Dashboard
Engineering ████████████████░░░ 78%
Finance ████████░░░░░░░░░░ 42%
Legal ██████░░░░░░░░░░░░ 31%
Support ████████████░░░░░░ 58%
R&D ██████████████████ 95%
Total compute this month: 847,000 Joules
Budget remaining: 153,000 Joules
Joules is Stera’s internal compute currency. Every GPU-second consumed by a pod costs Joules. Departments get budgets. Managers see exactly where AI compute goes. There are no surprise cloud bills — just predictable, transparent, internal cost accounting.
The CFO sees a dashboard, not an invoice from a cloud provider. When a department needs more compute, you allocate more Joules — not more budget to a third-party API.
Enterprise customers fine-tune pods on their own proprietary data — entirely on-premise. Select a base model, upload your training data, configure parameters, and the GPU Pool allocates training compute. Your data never leaves the building. The trained model deploys directly to the Pod Roster.
A legal firm trains its contract analyst on 10 years of its own contracts. A hospital trains its medical scribe on its own clinical documentation style. A manufacturing company trains its quality inspector on its own defect library. The model becomes expert in your specific domain — not a generic approximation of it.
AI Center Server
─────────────────────────────────────────
Hardware Multi-GPU rack server
GPU Pool 4× A6000 48 GB = 192 GB (configurable)
Operating Sys. SteraOS AI Center
Modules GPU Pool · Model Depot · Inference Engine
Pod Roster · Joules Economy · Training Engine
Security RBAC · SSO · audit logging
API Enterprise API Gateway (HTTPS + WebSocket)
Power ~1,200W
Per Desk
─────────────────────────────────────────
Stera Terminal Silent thin client, €99-149
Stera Mobile Voice-operated field pad
Software
─────────────────────────────────────────
Software Pods Co-developed with your org
SteraLink Desktop / mobile app (free)
Dashboard Full AI Center management
Pricing
─────────────────────────────────────────
AI Center from €15,000
Terminals €99-149 each
Monthly fees €0
Per-token costs €0
Need multi-site federation with data sovereignty per region? That’s the large enterprise tier. Starting smaller? Individual machines work just as well.