Objective of this project is to run additional Large Language Models locally in our
existing infrastructure within the National Research Council Canada’s Azure Cloud. The
Large Language Model should provide an API with a means to be queried against, and
an appropriate response returned. There should be no data exfiltration, with all
computation occurring locally. For data sovereignty and security, the provider of the
LLM should primarily operate within Canada. The provided LLM is expected to be
general use and meeting common industry benchmark metrics (MMLU, GPQA, IFEval,
ChatRAGBench, StrategyQA, and others) within 5 percentage points or better of
previous or current generation industry models (GPT-4o, Llama 3.3 70B, Mistral Large
2, DeepSeek V3, Gemini 2.0 Pro, etc.). The model should also be built and trained by
the vendor from the ground up, without relying on tuning or re-training any existing
models. The provider will be engaged for licensing and installation assistance.
The NRC is exploring the deployment of Large Language Models (LLMs) within its
secure cloud computing environment, seeking to operate LLMs locally and ensure that
all data processing and computation occur entirely within the controlled environment,
maintaining strict data sovereignty and security standards.
Preliminary technical work has been completed to establish the foundation for this
undertaking, including the setup of a GPU-based virtual machine that is able to
communicate with the in-house developed LLM service delivery platform.
This initiative aligns with NRC’s ongoing commitment to advancing secure, sovereign AI
capabilities that support Canadian innovation, protect sensitive information, and ensure
compliance with government data management standards.
