# NeuronPlant / Factory templates

Private AI, training, AI-as-a-Service, sovereign, and large-scale factories NeuronPlant can deliver.

Canonical HTML: https://neuronplant.com/ai-factories
Collection file: https://neuronplant.com/llms/factory.txt

NeuronPlant is not an NVIDIA Cloud Partner. NVOnline packs are named, not reprinted. Customer names and customer specifications publish only after written approval.

# Enterprise Private AI Factory

Private enterprise AI / RAG / inference.

A controlled environment for enterprise models, retrieval, and inference, inside the organization's security and data boundary.

## Pipeline

- Data Sources
- Ingestion
- Object / File Storage
- Vector / RAG Layer
- Compute
- Inference Platform
- Enterprise Applications
- Operate

## Infrastructure

- Site: Inside the organization's security and data boundary.
- Power: Rack-level redundancy, typically 20-40 kW/rack
- Cooling: Air or targeted liquid depending on density
- Compute: Accelerators for NIM / RAG serving. GPU plant: NVIDIA DGX B200 or NVIDIA DGX B300, or HGX. TPU and other ASICs when that is the job.
- Network: Spectrum-X East-West compute fabric, dedicated storage/data path, separate North-South front-end
- Structured cabling: Labeled LC/MPO plant. Management/OOB on its own patch field. Factory-tested trunks. Polarity map before first optic.
- Storage: Object for documents + NVMe or memory vector index. Model weights on local NVMe.
- Platform: Sources → ingest → RAG / inference → enterprise applications.
- Operate: Docker / Kubernetes cell. Observe as part of the plane.

## Considerations

- Dataset size and retention
- Growth of concurrent sessions
- Availability target
- Security and data residency
- Latency to enterprise applications
- Data lifecycle and audit
- Whether the existing hall plant can take 400G without a recable
- How the serving plane is observed after handover

Canonical: https://neuronplant.com/ai-factories/private-ai

# High-Performance Training Factory

High-performance accelerator training infrastructure.

A tightly coupled training cluster: fabric, storage bandwidth, and cooling sized to the job, not the idle rack. GPU is the common case. TPU and other ASICs when that is the domain.

## Pipeline

- Datasets
- Staging / Parallel FS
- Training fabric
- Checkpoint Store
- Scheduler
- Experiment Platform
- Operate

## Infrastructure

- Site: Dedicated training hall we occupy. Floor, water, and power are gates of that room before the first node.
- Power: High-density feeds, often 40-120 kW/rack
- Cooling: Direct liquid cooling and CDU-backed rejection
- Compute: Accelerators in one training domain. GPU plant: NVIDIA DGX B200 or NVIDIA DGX B300 nodes, or NVIDIA DGX GB200 NVL72 or NVIDIA DGX GB300 NVL72 racks. TPU and other ASICs sized the same way, not as a catalog.
- Network: Quantum InfiniBand East-West compute fabric; dedicated storage/data fabric. Cited SuperPOD jobs on Capabilities.
- Structured cabling: Rail-aligned MPO trunks. Type-B polarity as a drawing, not a field guess. East-West compute, storage/data, and management/OOB on separate trays. IL and polarity records before first job.
- Storage: HPS parallel FS sized to NVIDIA SuperPOD guidance (>40 GB/s per NVIDIA DGX B300 node; >80 GB/s on XDR RA) plus a checkpoint burst target
- Platform: Datasets → staging → train fabric → checkpoints → scheduler.
- Operate: 3+3 Kubernetes cell. Observe the fabric and the job.

## Considerations

- Model scale and parallelism strategy
- Job mix vs dedicated cluster
- Checkpoint frequency and recovery
- Fabric congestion under all-reduce
- Structured cabling: polarity, slack, and whether the hall can be tested
- Facility power and water lead times
- Expansion without re-architecture
- Who watches the fabric and the job after first run

Canonical: https://neuronplant.com/ai-factories/training

# Multi-Tenant Compute Factory

Multi-tenant accelerator infrastructure designed for service providers.

A factory built to sell capacity: isolation, metering, rapid provisioning, and a hall-partner model that can grow in cells.

## Pipeline

- Onboarding
- Tenancy / Isolation
- Accelerator pools
- Shared Fabric
- Platform Control Plane
- Billing / Observability
- Operate

## Infrastructure

- Site: Colo or owned hall from a hall partner, grown in cells. Not a NeuronPlant campus.
- Power: Cell-based capacity, N+1 at hall and rack
- Cooling: Liquid-ready rows for density growth
- Compute: Pooled accelerators. GPU nodes with MIG / tenant slicing where applicable.
- Network: Segmented Spectrum-X or Quantum fabrics: East-West compute isolated from tenant North-South, noisy-neighbor controls
- Structured cabling: Per-plane trays so a tenant storage change cannot yank an East-West compute trunk. Labels that survive a night shift.
- Storage: Per-tenant namespaces on a shared high-performance backend
- Platform: Onboarding → tenancy → accelerator pools → control plane → customer workloads.
- Operate: Shared control plane, per-tenant isolation, observe as a billed plane.

## Considerations

- Tenant isolation model
- Oversubscription policy
- Support and SLA boundaries
- Chargeback and telemetry
- Cable plant isolation between tenants and between planes
- Cooling headroom for densification
- Sovereign or regional cells
- Tenant telemetry without a dashboard shopping list

Canonical: https://neuronplant.com/ai-factories/ai-as-a-service

# Sovereign AI Factory

Private, controlled AI infrastructure for regulated organizations.

Infrastructure that can be operated, inspected, and expanded under a defined jurisdiction: power to platform, not only the model weights.

## Pipeline

- National / Org Data
- Controlled Ingest
- Sovereign Storage
- Private compute domain
- Governed Platform
- Authorized Workloads
- Operate

## Infrastructure

- Site: In-jurisdiction hall from a partner or custom-system supplier. Inspectable plant and operational control.
- Power: Facility-level independence and documented failover
- Cooling: Heat rejection the hall owner or custom-system supplier presents, with operational control in-jurisdiction.
- Compute: Dedicated accelerator domains, no shared cloud control plane
- Network: Air-gapped or tightly gated interconnects
- Structured cabling: Inspectable plant: labeled, tested, as-built. No undocumented jumpers on the air-gap.
- Storage: In-country datasets, encryption and key custody
- Platform: National / org data → controlled ingest → governed platform → authorized workloads.
- Operate: In-jurisdiction runtime cell. Observe stays inside the boundary.

## Considerations

- Data residency and custody
- Personnel and operational control
- Supply chain of compute
- Auditability of the stack
- Independent support model
- Long-term expansion path
- Observe stays inside the jurisdiction

Canonical: https://neuronplant.com/ai-factories/sovereign-ai

# Large-Scale AI Factory

Large-scale accelerator environment with liquid cooling and high-speed networking.

A hall-scale accelerator program delivered with a hall partner or custom-system supplier: megawatts and liquid as hall constraints, 800G fabrics and the GPU / TPU / ASIC plant as NeuronPlant's job. Not NeuronPlant as the data-center contractor.

## Pipeline

- Datasets / ingest
- Hall-scale HPS
- Accelerator supercluster
- Checkpoint / archive
- AI platform
- Production workloads
- Operate

## Infrastructure

- Site: MW-class hall from a partner or custom-system supplier. Utility and water are gates of that room, not a NeuronPlant civil works package.
- Power: MW-class, staged to utility and on-site distribution
- Cooling: Facility water, CDUs, rear-door or direct-to-chip, N+1 rejection
- Compute: Accelerator halls at multi-pod scale. GPU plant: NVIDIA DGX GB200 NVL72 / NVIDIA DGX GB300 NVL72. TPU and other ASICs when that is the job.
- Network: Quantum-X or Spectrum-X East-West scale-out, dedicated storage/data fabric, North-South for users, management/OOB independent
- Structured cabling: SMF/MPO campus plant inside the CIN 500 m class from the public NVIDIA DSX overview. Overhead baskets. Trays grouped by plane: East-West compute, storage/data, North-South, management/OOB. OTDR on backbone. As-builts before first power-on.
- Storage: Multi-tier: scratch, dataset, checkpoint, archive
- Platform: Datasets → hall HPS → supercluster → checkpoints → production workloads.
- Operate: Cell-by-cell Kubernetes. Observe before first training run, not after.

## Considerations

- Utility interconnection timeline (hall owner or custom-system supplier)
- Water and heat-rejection path
- Phased pod delivery
- Logistics of high-density racks
- Fiber plant reach, polarity, and test records at hall scale
- Operations before first training run
- Cell-by-cell expansion
- Observe before first training run

Canonical: https://neuronplant.com/ai-factories/large-scale