The integration of large language models (LLMs) directly into industrial production environments is gaining traction, offering significant advantages in terms of data analysis and process optimization.

Real-world implementations

Some plant engineers are already successfully implementing quantized LLMs such as Mistral 7B and Llama 8B on Jetson Orin devices for crucial tasks. A concrete example is anomaly detection in data from vibration sensors, operating 24/7, with over 140,000 sensor readings per hour. A food plant has kept its setup running for 11 consecutive months, with costs limited to energy consumption.

Data sovereignty and compliance

A key aspect of these implementations is the need to keep production data within the company perimeter. Legal restrictions and the protection of trade secrets often prevent the use of external cloud services. In industries such as semiconductor manufacturing, yield parameters are considered confidential information, making data analysis outsourcing inadmissible.

For those evaluating on-premise deployments, there are trade-offs to consider carefully. AI-RADAR offers analytical frameworks on /llm-onpremise to support this decision-making process.