Benchmarking Local LLMs for Natural-Language-to-SQL Querying in Biopharmaceutical Manufacturing: An Empirical Benchmark on Consumer-Grade Hardware
arXiv:2606.01338v1 Announce Type: new Abstract: Biopharmaceutical manufacturing organizations operate under regulatory frameworks such as FDA guidance, EU Good Manufacturing Practice (GMP), and the EU AI Act, which can restrict the use of cloud-based artificial intelligence systems. Locally deployed large language models (LLMs) offer a privacy-preserving alternative, but their suitability for pharmaceutical manufacturing tasks remains underexplored. This study evaluates four open-source LLMs (Qwen 2.5 Coder 7B, Llama 3.1 8B, Mistral 7B, and Meditron 7B) deployed locally via Ollama for natural-
Regulatory frameworks like the EU AI Act and existing FDA/GMP guidelines are increasingly influencing AI deployment, pushing for privacy-preserving solutions at a time when LLM capabilities are rapidly advancing for local deployment.
This study highlights the growing need for on-premise AI solutions in highly regulated industries, signalling a significant market opportunity and technical challenge for local LLM development and deployment.
The viability of local LLMs for sensitive industrial applications, particularly in biopharmaceutical manufacturing, is being empirically validated, reducing reliance on public cloud infrastructure.
- · Open-source LLM developers
- · On-premise AI solution providers
- · Biopharmaceutical companies
- · Hardware manufacturers for local inference
- · Public cloud-based AI providers (for regulated sectors)
- · Companies without robust local AI strategies
Increased adoption of local LLMs in regulated industries to meet data privacy and security requirements.
Development of specialized, resource-efficient LLMs tailored for specific industrial applications and edge devices.
A fragmentation of the AI market between cloud-based general-purpose AI and highly customized, locally deployed industrial AI.
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Read at arXiv cs.CL