Industrial AI — Deployment Guide
On-Premise LLM for Manufacturing: Complete Deployment Guide
An on-premise LLM for manufacturing runs on servers inside your own infrastructure — inside your plant network, private data centre, or dedicated cloud environment — with zero data leaving your control. Every prompt, every response, and every audit log stays within your perimeter. It is the highest-trust deployment model for factories that handle classified operational data, IP-sensitive process knowledge, or operate under strict regulatory frameworks.
When on-premise LLM deployment is the right choice
On-premise deployment is the most operationally complex of the three deployment models. It is not always the right choice — but when your situation matches the criteria below, it is the only choice that adequately addresses your requirements.
Choose on-premise when:
Your data classification policy prohibits cloud processing of operational data
You operate under ITAR, defence, or government framework requirements
Your plant is in a region with strict data sovereignty laws (e.g., EU manufacturing subsidiaries with restricted cross-border transfer)
You need to operate in air-gapped or partially air-gapped environments
Your IT security team requires full audit access to the AI processing environment, including the inference infrastructure
Your production data contains trade secrets that would provide meaningful competitive advantage if exposed
Consider private API instead when:
Full hardware management is operationally complex for your IT team and procurement timelines are long
You need to deploy in weeks rather than months and hardware procurement is a bottleneck
Elasticity matters more than absolute data isolation — you have highly variable query volumes
What on-premise LLM deployment looks like in a factory
Below is the step-by-step sequence of what physically and technically happens during an on-premise deployment of DBR77 Vector. The process is more structured than a SaaS signup — but simpler than a typical on-premise ERP deployment.
Hardware provisioning
GPU servers (or CPU inference for smaller models) are provisioned in your server room, data centre, or edge location. Hardware requirements are assessed during the pilot phase — most plants already have appropriate rack infrastructure.
Model installation
The LLM weights are transferred to your environment via an encrypted, offline package. After installation, the vendor has no remote access to the running model. Your environment is the model's only home.
API endpoint setup
The model is exposed as an internal REST API accessible only within your plant network. Your IRIS, MES, ERP, and CMMS systems call this endpoint — from their perspective, it behaves like any internal microservice.
Integration configuration
Factory systems are configured to send operational context — production data, machine states, quality readings — to the API and receive structured AI recommendations in return. Pre-built connectors accelerate this for SAP, Oracle, Siemens, and common MES platforms.
Governance setup
Audit logging, role-based access control, approval workflows, and incident response protocols are configured before the system goes live. Governance setup is not optional — it is what distinguishes a production-ready deployment from a prototype.
Important: DBR77 Vector's on-premise deployment does not require a persistent connection to DBR77 systems after installation. The model runs autonomously within your infrastructure. Optional updates are delivered as encrypted offline packages on a schedule you control.
Hardware requirements for on-premise LLM in manufacturing
Hardware requirements depend on query volume, latency expectations, and whether you need redundancy. Below are concrete specifications for the most common deployment configurations.
Production inference (standard)
Recommended for most manufacturing deployments
- —GPU: Minimum 2× NVIDIA A100 80GB per inference node (H100 preferred for higher throughput and lower latency)
- —Memory: 512GB+ RAM per node recommended for full 120B parameter model at FP16
- —Storage: 240GB+ NVMe for model weights at FP16; 60–80GB for quantized INT4/INT8 variants
- —Network: Internal 10GbE between model server and plant systems; no public internet required after deployment
- —Redundancy: Active-passive failover with a second inference node recommended for production-critical use
Edge inference (quantized, CPU or lower-spec GPU)
For lighter workloads or edge locations
- —CPU-based inference with INT4/INT8 quantization is viable for query volumes under 50/hour
- —Storage: 60–80GB for quantized model weights
- —RAM: 128GB minimum for CPU inference of quantized 120B model
- —Throughput: Significantly lower than GPU inference — suitable for non-time-critical use cases (shift reports, compliance drafts)
- —Latency: 30–120 seconds per query on CPU vs. 3–8 seconds on A100 GPU
Most plants already have appropriate server infrastructure for on-premise LLM. Hardware compatibility is assessed during the pilot phase, and specific recommendations are provided before any procurement commitment.
Integrating an on-premise LLM with factory systems
There are three integration patterns, ordered from lowest risk and fastest to deploy (read-only) to most deeply embedded in operational workflows (embedded integration). Most organisations start with read-only and expand over time.
Pattern 1
Read-only integration
The LLM queries your MES, ERP, CMMS, and SCADA systems for context, then returns recommendations to a user interface. Humans read the recommendations and decide what to do. The model cannot write to any source system. This pattern has no integration risk and can be operational within days of hardware provisioning.
Pattern 2
Bidirectional integration
LLM recommendations can trigger workflow actions — creating a maintenance ticket, flagging a quality exception, initiating a procurement request — after a human approves the action. The model recommends, a human confirms, the system acts. The approval workflow is configurable per action class.
Pattern 3
Embedded integration
LLM reasoning runs inside existing operational workflows. Example: a quality inspection triggers an automatic AI analysis of the result in context of recent production parameters; the model produces a pass/fail recommendation and a cause hypothesis; the operator sees both and confirms. The model is part of the process, not a separate tool.
DBR77 Vector integrates via REST API with pre-built connectors for SAP ECC/S4HANA, Oracle EBS, Siemens MindSphere, and custom MES/SCADA systems. Integration timeline: 2–6 weeks depending on system complexity and pattern chosen.
Governance and audit for on-premise LLM
Governance is not a post-deployment task — it is part of the deployment itself. An on-premise LLM without governance is a liability. An on-premise LLM with proper governance is a documented, auditable, and improvable operational asset.
Complete input/output logging
Every prompt, every response, timestamped, with user identity and system source. This is not optional for ISO 27001 or IEC 62443 compliance.
Role-based access control
Define which users and systems can query which data domains. Maintenance staff should not have access to financial reasoning; procurement should not see quality-sensitive process parameters.
Approval workflows
For any decision class that triggers a downstream action — maintenance scheduling, quality holds, procurement orders — configure a human approval step before execution.
Regular output audits
Compare model recommendations against known-good outcomes from your operational history. This produces a measurable accuracy baseline and surfaces any recommendation drift over time.
Incident classification
Define what constitutes a model incident — a recommendation that was rejected, that caused an unexpected outcome, or that contradicted expert judgment — and establish a response protocol for each severity level.
Model version control
Every update to the deployed model should be version-controlled, with the previous version retained for rollback. Audit records should reference the model version that produced each recommendation.
Governance is easier on-premise than in shared cloud environments because you own the entire audit trail. Every recommendation can be traced to the exact model version, input data, and user identity — which is what ISO 27001, IEC 62443, and most enterprise security frameworks require when validating AI-assisted operational decisions.
Frequently asked questions
Can an on-premise LLM work in a plant with limited IT staff?
Yes, but the initial setup requires IT involvement. Once deployed, DBR77 Vector runs with minimal maintenance — model updates are optional and scheduled, not automatic. The ongoing operational burden is comparable to managing any internal server application.
Does on-premise LLM require a connection back to the vendor after deployment?
No. DBR77 Vector's on-premise deployment operates fully autonomously after installation. There is no telemetry or callback to DBR77 systems. Optional model updates are delivered as encrypted packages you install on your own schedule.
How does on-premise LLM handle power outages or server failures?
The LLM is a stateless inference service — it does not store session state. In a failover event, queries wait or are retried. DBR77 Vector supports active-passive failover configurations. For critical production environments, we recommend redundant hardware with automatic failover.
Can we run on-premise LLM on our existing server infrastructure?
Possibly, but GPU-capable servers are required for full-model inference. If your existing infrastructure is CPU-only, quantized (INT4/INT8) inference is possible with reduced throughput. We assess hardware compatibility during the pilot phase and provide specific recommendations before any commitment.
What is the total cost of ownership of an on-premise LLM vs SaaS AI?
On-premise TCO includes: hardware (one-time, typically €80k–€250k depending on configuration), installation and integration (one-time), and annual support. SaaS AI costs are per-query and scale with usage. For plants running 500+ daily AI queries, on-premise TCO is typically lower within 18–24 months. For lighter usage, private API deployment is more cost-effective.
How do we validate that the on-premise LLM is performing correctly?
DBR77 Vector includes a built-in evaluation framework: you provide reference decisions (known-good outcomes from your operational history), and the model's recommendations are compared against them. This gives you a measurable accuracy baseline before going live — and a benchmark for ongoing monitoring.
Assess on-premise deployment for your plant
DBR77 Vector supports full on-premise deployment with no persistent external connection after installation. Talk to a manufacturing AI specialist about your infrastructure requirements and timeline, or review the full deployment options.