What is local AI? Models on your own infrastructure

Take local AI from pilot to reliable operations? WZ-IT designs hardware, model serving, identity, knowledge retrieval, monitoring and backup as one platform, on-premises or on dedicated infrastructure. Explore managed AI

Local AI is more than a language model running on a GPU. It becomes production-ready only with a defined trust boundary, identity, knowledge sources, observability, updates and recovery. This article explains the architecture and when on-premises, dedicated self-hosting or hybrid operation makes sense. As of August 2026.

Table of contents

• Cloud AI or local AI
• What "local" actually means
• Why companies run it locally
• What belongs to local AI
• When local AI makes sense
• Where local systems still communicate externally
• From pilot to production
• How WZ-IT implements local AI
• Sources

Cloud AI or local AI

Most people know AI through cloud services: an application sends a request to an externally operated model endpoint. Depending on provider, product and configuration, content and metadata are processed in the agreed region. This is convenient and scales quickly, but delegates part of the technical control.

Local AI moves inference into a controlled environment. Users can keep the same chat or API experience, while the organisation decides model, version, network paths, access and retention. This creates control but also operational responsibility.

What "local" actually means

"Local" does not necessarily mean "in your own server room". What is meant is: on infrastructure you control. That can be:

• a GPU server in your own data center or server room (on-premise),
• a virtual machine on Proxmox or bare metal,
• a server at a European hoster that is under your control.

The decisive concept is the defined trust and operating boundary. On-premises normally means hardware at the organisation's site. Self-hosted means that the stack is operated by or for the organisation. Dedicated infrastructure can sit at a European hosting provider. These terms should not be used interchangeably because access, responsibility and resilience differ.

Why companies run it locally

Three reasons drive the switch:

• Data protection and sovereignty - data paths, access and retention can be constrained more tightly. Whether external transfers disappear entirely depends on the whole platform. This strengthens the technical basis for data protection and AI sovereignty but does not replace legal assessment.
• Cost control - capacity cost is predictable, while hardware, energy, redundancy and operations must also be included. Local inference can suit stable base load; cloud can remain cheaper for low or highly variable demand.
• Independence - no lock-in to the price, model or license changes of a single provider. You decide which model runs when.

The trade-off in detail - when cloud, when your own hardware - is shown in Cloud AI vs. self-hosted.

What belongs to local AI

Local AI is a platform with several layers:

• Compute and storage - GPU, CPU, VRAM, model storage, document storage and backup.
• Inference server - for example Ollama for compact setups or vLLM for throughput-oriented APIs.
• Gateway - common endpoints, model routing, limits and fallbacks.
• Identity and network - SSO, roles, secrets, segmentation and controlled administration.
• Application and knowledge - UI, APIs, RAG, citations and permission checks before retrieval.
• Operations - metrics, data-minimised traces, evaluation, updates, rollback, backup and incidents.

Only this interplay turns a model demo into a multi-user production service with defined rights and availability.

When local AI makes sense

Local AI pays off especially when at least one of these applies: you process sensitive or regulated data (law, health, public sector, industry), you have continuous, high usage where token costs weigh in, or you want to be independent of a single US provider.

For sporadic and suitable use, a cloud service may remain the simpler entry. Local AI becomes particularly relevant when data paths need tight boundaries, systems must integrate with existing identity and networks, or base load is predictable. Decide through a pilot rather than a blanket privacy or cost claim.

Where local systems still communicate externally

“The model is local” does not mean the platform is offline. Typical outbound paths include model and container downloads, telemetry, crash reporting, web search, external embeddings, cloud observability, email, OCR, speech services, remote support and off-site backups. These may be useful and lawful, but should be visible, approved and constrained.

Ollama, for example, provides a setting to disable its cloud features. Egress policies and outbound-connection tests provide additional assurance.

From pilot to production

1. Define use case, user groups, data classes and measurable quality targets.
2. Compare two or three exact model releases on real tasks and documents.
3. Measure VRAM, context, parallelism, latency and peak demand.
4. Include identity, permissions, RAG and logging in the pilot.
5. Test updates, failure, backup restore and model rollback.
6. Only then size hardware and select a production service level.

This avoids buying oversized hardware for an unsuitable model or building a strong demo without an operating path.

How WZ-IT implements local AI

WZ-IT can integrate the platform into existing infrastructure or operate it as managed AI. The AI Cube supports compact on-premises scenarios; GPU servers and LLM hosting cover larger or centralised model services.

The work goes beyond starting a model: network, identity, model server, knowledge sources, monitoring, backup and further development become one operable system. An internal AI assistant with sources and permissions can build on top.

Sources

• Ollama documentation: local operation, cloud controls and parallelism
• vLLM documentation: OpenAI-compatible inference server
• LiteLLM documentation: routing and load balancing
• European Commission: data-protection obligations and DPIAs