Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organization retains full control over access rights, data management, and deployment strategies. This approach enables the support of internal AI tools using either the company's own infrastructure or a dedicated hosted environment.
Where private LLMs are useful
- Internal knowledge search: Assisting employees in locating information across company documents, standard operating procedures, and technical manuals. Responses must strictly adhere to existing document permission structures.
- Document processing: Enabling the summarization of reports, extraction of key data, and drafting of content based on internal materials. Manual oversight remains critical in instances where errors could impact business decisions.
- Software development: Facilitating code explanation, documentation creation, and development tasks while ensuring that 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 alleviates the need for physical infrastructure management, it necessitates a thorough review of provider access protocols and data residency.
- Cost and capacity: Operating expenses encompass hardware, energy, hosting services, and maintenance. The necessary capacity is determined by factors such as model size, document length, and the number of concurrent users.
- Performance and quality: Smaller models typically demand fewer computational resources. It is advisable to test these models against representative tasks to verify that their accuracy and response times meet business standards.
- Security and maintenance: A private deployment strategy does not inherently guarantee security. Continued attention to access controls, logging, system updates, and the protection of stored conversations is essential.
How NobleConsul can help
- NobleConsul can assist in evaluating specific use cases, comparing various deployment options, and validating models against defined business requirements.
- NobleConsul is also capable of provisioning server hardware for private LLM deployments, selecting appropriate GPU, memory, storage, and networking configurations based on workload demands and budget constraints.
- Support services may include integration with existing internal systems, as well as planning for ongoing operations, maintenance, and future capacity enhancements.
Private LLM Consulting Frequently Asked Questions
When should an organisation consider private LLM consulting?
Organizations should consider this when they need to leverage AI with sensitive data—such as internal documents, source code, or customer information—without depending on public AI services. It is also highly beneficial when transitioning an AI pilot to a production environment and when assessing the optimal models, hardware, and infrastructure.
What hardware is needed to run a private LLM?
GPU memory requirements are directly tied to the model's size. Smaller models (7–8 billion parameters) can function on a single GPU with 16–24 GB of memory, whereas larger models (70 billion parameters) typically require around 40 GB or more, often distributed across multiple GPUs. Higher user concurrency and longer documents will also necessitate additional memory capacity.
How much does it cost to run an on-premise LLM?
Costs vary based on model size, user count, and performance specifications. Smaller models may operate on a single GPU server, while larger ones require more expensive hardware. Recurring costs include electricity, cooling, software updates, and maintenance. Conducting a pilot with real-world workloads is an effective way to estimate the total investment.
What is the difference between a private LLM and ChatGPT Enterprise?
A private LLM runs on the organization's own infrastructure, providing complete control over data processing locations and methods. In contrast, ChatGPT Enterprise is hosted by OpenAI under specific contractual terms. Private LLMs generally utilize open-source models like Llama, Mistral, or Qwen, which the organization is responsible for managing and maintaining.