Private LLM

A private large language model (LLM) runs in an environment where an organization controls access, data handling, and deployment. It can support internal AI tools using company infrastructure or a dedicated hosted environment.

Where private LLMs are useful

• Internal knowledge search: Help employees find information in company documents, procedures, and technical manuals. Answers should respect existing document permissions.
• Document processing: Summarize reports, extract information, and prepare drafts from internal material. Human review remains necessary where mistakes could affect business decisions.
• Software development: Support code explanation, documentation, and development tasks while keeping source code within an approved environment.

Deployment choices and trade-offs

• Location and control: On-premise deployment provides direct control over hardware and network access. Dedicated hosting reduces physical infrastructure work but requires checking provider access and data location.
• Cost and capacity: Hardware, electricity, hosting, and maintenance contribute to operating costs. Required capacity depends on model size, document length, and simultaneous users.
• Performance and quality: Smaller models generally need fewer resources. Test them against representative tasks to determine whether their accuracy and response times are sufficient.
• Security and maintenance: Private deployment alone does not guarantee security. Access controls, logging, updates, and protection of stored conversations still need attention.

How NobleConsul can help

• NobleConsul could help assess use cases, compare deployment options, and test models against business requirements.
• NobleConsul can also provision server hardware for private LLM deployments, with GPU, memory, storage, and networking choices based on workload and budget.
• Support could include integration with internal systems and planning ongoing operation, maintenance, and capacity upgrades.