Private LLM Consulting Services

A private large language model (LLM) operates within a controlled environment where the organization retains full authority over access permissions, data management, and deployment strategies. This setup enables the support of internal AI tools by leveraging either in-house company infrastructure or a dedicated hosted environment.

Use cases for private LLMs

Internal knowledge retrieval: Facilitates employee searches across company documents, procedures, and technical manuals. It ensures that responses strictly adhere to existing document permission structures.
Document processing: Assists in summarizing reports, extracting key details, and drafting content from internal materials. However, human oversight remains critical in scenarios where errors could impact business decisions.
Software development: Supports tasks such as code explanation, documentation, and development workflows, all while keeping source code secured within an approved environment.

Deployment options and considerations

Location and control: On-premise deployment offers direct oversight of hardware and network access. Conversely, dedicated hosting minimizes physical infrastructure responsibilities but requires verification of provider access policies and data residency.
Cost and capacity: Operational 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 typically demand fewer resources. These should be tested against representative tasks to verify that accuracy and response times meet business needs.
Security and maintenance: Private deployment does not inherently ensure security. Continuous attention is required for access controls, logging, updates, and the protection of stored conversation data.

How NobleConsul can help

NobleConsul supports the evaluation of use cases, comparison of deployment strategies, and testing of models against specific business requirements.
The team can also provision server hardware tailored for private LLM deployments, selecting GPU, memory, storage, and networking options based on workload demands and budget constraints.
Services may extend to integration with internal systems and the planning of ongoing operations, maintenance, and capacity upgrades.

Private LLM Consulting Frequently Asked Questions

When should an organization consider private LLM consulting?

This is recommended when an organization intends to utilize AI with sensitive data—such as internal documents, source code, or customer information—without depending on public AI services. It is also beneficial when transitioning an AI pilot to production and determining the appropriate 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) can operate on a single GPU with 16–24 GB of memory. In contrast, a larger model (70 billion parameters) requires approximately 40 GB or more, often necessitating multiple GPUs. Increased concurrent user activity or the processing of longer documents may demand additional memory.

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

Costs vary based on model size, user count, and required response times. A basic internal AI assistant may function on a single GPU server, whereas larger models require more robust and expensive hardware. Additionally, ongoing expenses such as electricity, cooling, software updates, and maintenance must be factored in. Conducting a pilot with real 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 the organization, providing enhanced control over data storage and processing locations. ChatGPT Enterprise, however, is hosted by OpenAI, with data processed on their infrastructure under their contractual terms. Private LLMs frequently utilize open-source models like Llama, Mistral, or Qwen, for which the organization assumes responsibility for management, maintenance, and updates.