Private LLM Consulting Services

A private large language model (LLM) operates within an environment where the organisation retains full authority over access, data management, and deployment strategies. It enables internal AI applications to run on company infrastructure or within a dedicated hosted setup.

Key Applications for Private LLMs

Internal knowledge retrieval: Empowers employees to efficiently locate information across corporate documents, standard operating procedures, and technical guides, ensuring responses adhere to existing permission structures.
Document management: Facilitates the summarisation of reports, extraction of key insights, and drafting of content from internal assets, with human oversight remaining critical where errors could impact business decisions.
Software engineering: Assists with code interpretation, documentation, and development workflows, all while ensuring source code remains within an authorised, secure environment.

Deployment Options and Considerations

Location and governance: On-premise setups offer direct oversight of hardware and network access. While dedicated hosting minimises physical infrastructure management, it necessitates careful verification of provider access policies and data residency.
Expenditure and scalability: Operational costs encompass hardware, power, hosting, and maintenance. The required capacity is determined by model size, document volume, and concurrent user load.
Performance and efficacy: Although smaller models demand fewer resources, it is essential to evaluate them against representative tasks to confirm that accuracy and response latency meet business standards.
Security and upkeep: Private deployment does not inherently ensure security. Continued focus is required on access controls, logging, updates, and the protection of stored interactions.

How NobleConsul Supports You

NobleConsul assists in evaluating use cases, contrasting deployment strategies, and validating models against specific business requirements.
They can procure server hardware tailored for private LLM environments, selecting GPU, memory, storage, and networking configurations based on workload demands and budget constraints.
Support services extend to integrating with internal systems and strategising ongoing operations, maintenance, and future capacity enhancements.

Frequently Asked Questions on Private LLM Consulting

When should an organisation consider private LLM consulting?

It is advisable when seeking to utilise AI with sensitive data—such as internal documents, source code, or customer information—without depending on public AI services. This is also beneficial when transitioning an AI pilot to production, requiring assessment of suitable models, hardware, and infrastructure.

What hardware is required to run a private LLM?

GPU memory is the primary requirement, dictated by the AI model's size. A smaller model (7–8 billion parameters) typically runs on a single GPU with 16–24 GB of memory. Conversely, a larger model (70 billion parameters) necessitates approximately 40 GB or more, often involving multiple GPUs. Increased concurrent users or longer document processing may demand additional memory.

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

Costs are influenced by model size, user volume, and response speed requirements. A small internal AI assistant may operate on a single GPU server, whereas larger models demand more robust and costly hardware. Recurring expenses such as electricity, cooling, software updates, and maintenance must also be factored in. Conducting a pilot with actual workloads is the most effective method for estimating total costs.

What distinguishes a private LLM from ChatGPT Enterprise?

A private LLM runs on infrastructure managed by your organisation, affording greater control over data storage and processing locations. ChatGPT Enterprise, however, is hosted by OpenAI, with data processed on its infrastructure under specific contractual terms. Private LLMs frequently utilise open-source models like Llama, Mistral, or Qwen, for which your organisation assumes responsibility for management, maintenance, and updates.