Private LLM Consulting Services

A private large language model (LLM) operates within an environment where the organisation retains full command over access rights, data management, and deployment strategies. This setup enables the development of internal AI tools leveraging corporate infrastructure or a dedicated hosted environment.

Use cases for private LLMs

• Internal knowledge retrieval: Enables employees to locate relevant information within company documentation, standard operating procedures, and technical guides, ensuring that responses adhere to established document permission hierarchies.
• Document handling: Facilitates the summarisation of reports, extraction of key data points, and drafting of content from internal sources, while maintaining a process for human review to validate accuracy where business decisions are at stake.
• Software engineering: Assists with code interpretation, documentation generation, and development workflows, ensuring that source code remains secured within sanctioned environments.

Deployment options and trade-offs

• Location and control: On-premise deployment offers granular control over hardware and network access, whereas dedicated hosting minimizes physical infrastructure management but necessitates verification of provider access policies and data residency.
• Cost and capacity: Operational expenses encompass hardware, power, hosting, and maintenance, with required capacity scaling based on model size, document volume, and concurrent user activity.
• Performance and quality: While smaller models typically demand fewer resources, it is essential to validate them against representative tasks to confirm that accuracy and response latency meet organisational standards.
• Security and maintenance: Private deployment does not inherently ensure security; continuous attention must be paid to access controls, logging, software updates, and the protection of stored conversational data.

How NobleConsul can help

• NobleConsul supports the assessment of specific use cases, comparison of deployment strategies, and testing of models against defined business requirements.
• NobleConsul also offers the provisioning of server hardware for private LLM deployments, tailoring GPU, memory, storage, and networking configurations to match workload demands and budgetary constraints.
• Support services may include integration with existing internal systems, as well as planning for ongoing operations, maintenance, and capacity scaling.

Private LLM Consulting Frequently Asked Questions

When should an organisation consider private LLM consulting?

When the organisation intends to utilise AI with sensitive data, such as internal documents, source code, or customer information, without dependence on public AI services. This approach is also beneficial when transitioning an AI pilot to production and evaluating the appropriate models, hardware, and infrastructure.

What hardware is required to run a private LLM?

Primarily GPUs with sufficient memory to accommodate the model. A compact model (7–8 billion parameters) operates on a single GPU with 16–24 GB of memory, whereas a large model (70 billion parameters) requires 40 GB or more, typically distributed across multiple GPUs. Increased user counts and longer documents necessitate greater capacity.

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

Costs vary based on model size, user count, and performance specifications. Smaller models can operate on a single GPU server, while larger models require more substantial hardware investments. Recurring costs include electricity, cooling, software updates, and maintenance. Conducting a pilot with actual workloads assists in estimating the total investment.

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

A private LLM runs on the organisation's own infrastructure, granting complete control over data processing methods and locations. In contrast, ChatGPT Enterprise is hosted by OpenAI under its specific contractual terms. Private LLMs generally employ open-source models such as Llama, Mistral, or Qwen, which are managed and maintained by the organisation.

Can a prototype be viewed before committing?

Yes. Following an initial consultation regarding needs and current systems, NobleConsul constructs a prototype for a real-world task, such as searching internal documents. This is accompanied by a proposal detailing implementation costs, total cost of ownership, and training requirements.

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

Your organisation. NobleConsul transfers ownership of the hardware, code, credentials, and documentation, subsequently revoking its own access. Any subsequent support is provided only with your explicit approval.