Private LLMs Consulting Services

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

Applications for Private LLMs

• Internal Knowledge Retrieval: Facilitate employee access to information within corporate documents, operational procedures, and technical manuals, ensuring that responses strictly adhere to existing document permission structures.
• Document Management: Assist in summarizing reports, extracting key insights, and drafting content from internal sources, while maintaining human oversight in scenarios where errors could impact business decisions.
• Software Engineering: Enhance code interpretation, documentation generation, and development workflows by keeping source code secure within an approved and controlled environment.

Deployment Options and Considerations

• Location and Governance: On-premise installation offers direct oversight of hardware and network access. Alternatively, dedicated hosting reduces the burden of physical infrastructure management but necessitates thorough verification of provider access protocols and data residency.
• Cost and Scalability: Total operational expenses encompass hardware, power consumption, hosting fees, and maintenance. The necessary capacity is determined by model size, document complexity, and the volume of concurrent users.
• Performance and Fidelity: Smaller models generally demand fewer computational resources. It is essential to benchmark these models against representative tasks to verify that their accuracy and response times meet business standards.
• Security and Upkeep: Private deployment is not an automatic guarantee of security. Continuous attention is required for access controls, activity logging, system updates, and the protection of stored conversation data.

NobleConsul's Support Capabilities

• NobleConsul can assist in evaluating specific use cases, comparing deployment strategies, and testing models against defined business requirements.
• NobleConsul is also capable of provisioning server hardware for private LLM deployments, offering tailored configurations for GPU, memory, storage, and networking based on workload demands and budget constraints.
• Support services may extend to integrating models with internal systems and planning for long-term operations, maintenance, and capacity enhancements.

Frequently Asked Questions on Private LLM Consulting

When is private LLM consulting recommended?

Consulting is advisable when an organization seeks to utilize AI for handling sensitive data—such as internal documents, source code, or customer records—without dependence on public AI services. It is also valuable when transitioning an AI pilot to production, ensuring the selection of appropriate models, hardware, and infrastructure.

What hardware specifications are required for a private LLM?

GPU memory requirements vary based on model size. Smaller models (7–8 billion parameters) can typically operate on a single GPU with 16–24 GB of memory, whereas larger models (70 billion parameters) require approximately 40 GB or more, often distributed across multiple GPUs. Higher concurrency and longer document lengths will necessitate additional memory resources.

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

Expenses are influenced by model size, user base, and performance demands. Smaller models can be hosted on a single GPU server, while larger ones require more substantial hardware investments. Recurring costs include electricity, cooling systems, software updates, and general maintenance. Conducting a pilot with real-world workloads is the best method for estimating the total investment.

How does a private LLM differ from ChatGPT Enterprise?

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