Private LLM Consulting Services

A private large language model (LLM) operates within a secure environment where the organization retains full authority over access rights, data management, and deployment strategies. This setup enables the support of internal AI tools utilizing either the company's existing infrastructure or a dedicated hosted environment.

Use cases for private LLMs

Internal knowledge retrieval: Facilitates employee access to information within corporate documents, procedural guidelines, and technical manuals, ensuring that responses strictly adhere to existing document permission hierarchies.
Document processing: Assists in summarizing reports, extracting key details, and drafting documents from internal sources. While automation enhances efficiency, human oversight remains essential in scenarios where inaccuracies could impact critical business decisions.
Software development: Supports code interpretation, documentation creation, and various development tasks, all while ensuring that source code remains contained within approved, secure environments.

Deployment options and considerations

Location and control: On-premise deployment offers direct oversight of hardware and network access. Alternatively, dedicated hosting minimizes the burden of physical infrastructure management, provided that the provider's access policies and data residency locations are thoroughly verified.
Cost and capacity: Operational expenses encompass hardware, electricity, hosting, and maintenance. The required capacity is determined by factors such as model size, document length, and the volume of concurrent users.
Performance and quality: Smaller models typically demand fewer resources. It is advisable to test these models against representative tasks to evaluate whether their accuracy and response times meet business standards.
Security and maintenance: Private deployment alone does not ensure security. Continuous attention is required for access controls, logging, updates, and the protection of stored conversational data.

How NobleConsul can help

NobleConsul can assist in evaluating specific use cases, comparing deployment alternatives, and testing models against defined business requirements.
The firm can also provision server hardware for private LLM deployments, offering configurations for GPU, memory, storage, and networking tailored to specific workloads and budget constraints.
Support services may include integration with internal systems, as well as strategic planning for ongoing operations, maintenance, and capacity scaling.

Private LLM Consulting Frequently Asked Questions

When should an organisation consider private LLM consulting?

This is recommended when an entity aims to leverage AI with sensitive data, such as internal documents, source code, or customer information, without relying on public AI services. It is also highly beneficial when transitioning an AI pilot to production and assessing the appropriate models, hardware, and infrastructure.

What hardware is needed to run a private LLM?

The primary requirement is GPU memory, which scales with the size of the AI model. A smaller model (7–8 billion parameters) can operate on a single GPU with 16–24 GB of memory. Conversely, a larger model (70 billion parameters) requires approximately 40 GB or more, often necessitating multiple GPUs. Concurrent usage by multiple users or processing of longer documents may further increase memory requirements.

How much does it cost to run an on-premise LLM?

Costs are influenced by model size, user count, and required response speed. A small internal AI assistant may function on a single GPU server, whereas larger models demand more powerful and costly hardware. It is also crucial to account for ongoing expenses such as electricity, cooling, software updates, and maintenance. Conducting a pilot with real-world workloads is the most effective method for estimating total costs.

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

A private LLM operates on infrastructure controlled by the organization, providing greater oversight over data storage and processing. In contrast, ChatGPT Enterprise is hosted by OpenAI, with data processed on its infrastructure under specific contractual terms. Private LLMs frequently utilize open-source models such as Llama, Mistral, or Qwen, for which the organization assumes responsibility for management, maintenance, and updates.