Private Large Language Models (LLMs) Consulting Services

A private large language model (LLM) operates within an environment where the organization retains full authority over access rights, data management, and deployment strategies. This approach enables the development of internal AI tools utilizing proprietary company infrastructure or a dedicated, isolated hosting environment.

Use Cases for Private LLMs

• Internal Knowledge Retrieval: Facilitates employee access to critical information within corporate documents, procedural guides, and technical manuals. Responses must strictly adhere to existing document access permissions.
• Document Processing: Enables summarization of reports, data extraction, and drafting from internal materials. Human oversight remains essential in scenarios where errors could impact business decisions.
• Software Development: Assists with code explanation, documentation creation, and development workflows, ensuring that source code remains contained within approved, secure environments.

Deployment Strategies and Trade-offs

• Location and Control: On-premise deployment offers direct oversight of hardware and network access. While dedicated hosting reduces physical infrastructure responsibilities, it necessitates verification of provider access protocols and data residency locations.
• Cost and Capacity: Operational expenses include hardware, electricity, hosting, and maintenance. Required capacity is determined by model size, document length, and the number of concurrent users.
• Performance and Quality: Smaller models typically demand fewer computational resources. Evaluate these models against representative tasks to verify that accuracy and response times meet operational standards.
• Security and Maintenance: A private deployment alone does not ensure security. Continuous attention is required for access controls, logging, software updates, and the protection of stored conversations.

How NobleConsul Can Assist

• NobleConsul can assist in evaluating specific use cases, comparing various deployment options, and testing models against defined business requirements.
• The firm can also provision server hardware for private LLM implementations, selecting GPU, memory, storage, and networking configurations based on workload demands and budget constraints.
• Additional support includes integration with internal systems and strategic planning for ongoing operations, maintenance, and future capacity upgrades.

Private LLM Consulting: Frequently Asked Questions

When should an organization consider private LLM consulting?

Organizations should consider this when they need to utilize AI on sensitive data—such as internal documents, source code, or customer information—without relying on public AI services. It is also critical when transitioning an AI pilot to production and determining the appropriate models, hardware, and infrastructure.

What hardware is required to run a private LLM?

The primary requirement is GPUs with sufficient memory for the specific model. A smaller model (7–8 billion parameters) can operate on a single GPU with 16–24 GB of memory. Larger models (70 billion parameters) require 40 GB or more, typically distributed across multiple GPUs. Increased user volume and longer documents necessitate higher capacity.

What is the cost of running an on-premise LLM?

Costs vary based on model size, user count, and performance requirements. Smaller models may run on a single GPU server, whereas larger ones require more expensive hardware. Recurring costs include electricity, cooling, software updates, and maintenance. A pilot project using real-world workloads helps estimate the total investment.

What is the difference between a private LLM and ChatGPT Enterprise?

A private LLM operates on the organization's own infrastructure, providing control over data processing methods and locations. ChatGPT Enterprise is hosted by OpenAI under its specific contractual terms. Private LLMs typically utilize open-source models such as Llama, Mistral, or Qwen, which are managed and maintained by the organization itself.

Can you see a prototype before committing?

Yes. Following an initial discussion about needs and existing systems, NobleConsul builds a prototype on a real task, such as searching internal documents. This includes a proposal detailing implementation costs, total cost of ownership, and training requirements.

Who owns the model, hardware, and data afterwards?

Your organization. NobleConsul transfers the hardware, code, credentials, and documentation to you, then revokes its own access. Any subsequent support is provided only with your explicit approval.