Active LabLocal & Private AIUpdated August 16, 2026

Local AI Infrastructure

Experiments with local GPU inference, private language models, embeddings, rerankers and manufacturing RAG architectures.

Problem

Many manufacturing environments cannot send engineering or production data outside their own network, which rules out the default cloud architecture entirely.

Hypothesis

The retrieval-critical stages of an industrial AI stack can run on customer-controlled hardware without an unacceptable loss in usefulness.

Architecture

Local GPU inference host in the lab.
Locally-served embedding and reranking models.
Private retrieval index.
Optional controlled-API generation for a hybrid split.

Experiment

Which stages benefit most from staying local, and what concurrency the hardware realistically supports.

Observations

Embedding and reranking give the most benefit per unit of local hardware.
Retrieval-grounded answering is usable locally; unconstrained reasoning is weaker.
Concurrency, not single-question latency, drives hardware sizing.

Technologies

Local GPU inference
Quantised models
Embedding models
Rerankers
Private RAG

Next steps

Measure concurrency properly and publish a sizing note.
Test the hybrid local-retrieval / controlled-generation split.

Related research

Local Inference for Private Industrial AIInvestigation →