Dedicated LLM Solutions 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 setup enables the support of internal AI applications using in-house infrastructure or a specialized hosted environment tailored to specific business needs.

Key Applications for Private LLMs

• Internal Knowledge Retrieval: Empowers employees to quickly locate relevant information within corporate documents, operational procedures, and technical guides. Crucially, the system must adhere to established document permission structures.
• Document Automation: Facilitates the summarization of reports, extraction of key data points, and drafting of content from internal sources. Human oversight remains essential in scenarios where errors could impact strategic business decisions.
• Software Engineering: Assists with code interpretation, documentation generation, and development workflows while ensuring all source code remains secure within an approved, controlled environment.

Deployment Strategies and Considerations

• Location and Governance: On-premise installations offer direct oversight of hardware and network security. Alternatively, dedicated hosting minimizes physical infrastructure management but necessitates rigorous verification of provider access protocols and data residency.
• Expenditure and Scalability: Operational expenses encompass hardware, energy, hosting fees, and upkeep. Determining necessary capacity relies on factors such as model complexity, document volume, and concurrent user load.
• Efficiency and Output Quality: Compact models typically demand less computational power. It is advisable to evaluate these against representative tasks to confirm that accuracy and response latency meet business standards.
• Security and Upkeep: A private deployment configuration does not automatically ensure security. Continuous attention is required for access controls, audit logging, software updates, and the safeguarding of stored interactions.

How NobleConsul Can Support You

• NobleConsul can assist in evaluating specific use cases, contrasting deployment alternatives, and benchmarking models against defined business requirements.
• The firm can also procure and configure server hardware for private LLM implementations, tailoring GPU, memory, storage, and networking specifications to match workload demands and budgetary constraints.
• Support services may extend to integrating these models with existing internal systems, as well as planning for long-term operations, maintenance schedules, and capacity expansions.

Frequently Asked Questions About Private LLM Consulting

When should an organization consider private LLM consulting?

This is recommended when intending to utilize AI for handling sensitive data—such as internal records, proprietary code, or client information—without dependence on public-facing AI services. It is also highly relevant when transitioning an AI pilot project to production status and determining the optimal models, hardware, and infrastructure architecture.

What hardware is required to operate a private LLM?

Primarily, GPUs with sufficient memory to accommodate the model are needed. A smaller model (with 7–8 billion parameters) can run on a single GPU equipped with 16–24 GB of memory. In contrast, a large model (70 billion parameters) requires at least 40 GB of memory, typically distributed across multiple GPUs. Increased user counts and longer document processing demands necessitate higher capacity.

What are the costs associated with running an on-premise LLM?

Expenses vary based on model size, user base, and performance expectations. While smaller models may operate on a single GPU server, larger models demand more costly hardware infrastructure. Recurring costs include electricity, cooling systems, software patching, and general maintenance. Conducting a pilot with real-world workloads is the best method for estimating the total investment.

How does a private LLM differ from ChatGPT Enterprise?

A private LLM resides on your organization's own infrastructure, providing full control over data processing locations and methods. ChatGPT Enterprise, conversely, is hosted by OpenAI under its specific contractual terms. Private LLM deployments typically leverage open-source models like Llama, Mistral, or Qwen, which are managed and maintained directly by your organization.

Is it possible to review a prototype before making a commitment?

Yes. Following an initial consultation regarding your needs and current systems, NobleConsul will develop a prototype focused on a practical task, such as internal document search. This is accompanied by a comprehensive proposal detailing implementation costs, total cost of ownership, and training requirements.

Who retains ownership of the model, hardware, and data post-implementation?

Your organization does. NobleConsul will transfer all hardware, code, credentials, and documentation to you and subsequently revoke its own access. Any future support services will only be conducted with your explicit authorization.