Private LLM Consulting Services

Running a private large language model (LLM) allows an organization to maintain full control over access, data handling, and deployment environments. This approach enables the support of internal AI tools using either proprietary company infrastructure or a dedicated hosted environment.

Where private LLMs are useful

• Internal knowledge search: Facilitate employee access to information within company documents, standard operating procedures, and technical manuals. It is essential that responses respect existing document permissions.
• Document processing: Generate summaries, extract key insights, and draft content from internal materials. Human oversight remains critical in scenarios where errors could impact business decisions.
• Software development: Assist with code explanation, documentation, and development tasks while ensuring source code remains within an approved, secure environment.

Deployment choices and trade-offs

• Location and control: On-premise deployment offers direct oversight of hardware and network access. While dedicated hosting reduces the burden of physical infrastructure management, it requires verification of provider access rights and data residency.
• Cost and capacity: Operating expenses include hardware, electricity, hosting, and maintenance. The necessary capacity is determined by model size, document length, and the number of concurrent users.
• Performance and quality: Smaller models generally require fewer resources. Evaluate these models against representative tasks to ensure their accuracy and response times meet operational needs.
• Security and maintenance: Private deployment does not inherently guarantee security. Continuous attention is required for access controls, logging, updates, and the protection of stored conversations.

How NobleConsul can help

• NobleConsul can assist in assessing use cases, comparing deployment strategies, and testing models against specific business requirements.
• NobleConsul also provisions server hardware for private LLM deployments, selecting GPU, memory, storage, and networking configurations based on workload demands and budget constraints.
• Support services may include integration with internal systems, as well as planning for ongoing operations, maintenance, and capacity upgrades.

Private LLM Consulting Frequently Asked Questions

When should an organisation consider private LLM consulting?

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

What hardware is needed to run a private LLM?

Primarily, GPUs with sufficient memory for the model are required. A small model (7–8 billion parameters) can run on a single GPU with 16–24 GB of memory. A large model (70 billion parameters) necessitates 40 GB or more, typically distributed across multiple GPUs. Increased user numbers and longer documents require additional capacity.

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

Costs vary based on model size, user count, and performance requirements. Smaller models may operate on a single GPU server, whereas larger ones demand more expensive hardware. Ongoing expenses include electricity, cooling, software updates, and maintenance. Conducting a pilot with real workloads helps estimate the total investment accurately.

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

A private LLM operates on an organization's own infrastructure, granting control over how and where data is processed. In contrast, ChatGPT Enterprise is hosted by OpenAI under its contractual terms. Private LLMs typically utilize open models such as Llama, Mistral, or Qwen, which the organization manages and maintains.

Can you see a prototype before committing?

Yes. Following an initial discussion about your needs and current systems, NobleConsul develops a prototype focused 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 retains ownership. NobleConsul transfers the hardware, code, credentials, and documentation, then revokes its own access. Any subsequent support is provided only with your approval and utilizes access under your control.