Private Large Language Models 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 the creation of internal AI solutions powered by corporate infrastructure or a dedicated hosted platform.
Key Applications of Private LLMs
- Internal Knowledge Retrieval: Facilitates efficient discovery of information within corporate documentation, procedural guides, and technical manuals. Crucially, the system respects existing permission structures attached to these documents.
- Document Automation: Streamlines the summarization of reports, extraction of key insights, and generation of drafts from internal assets. Human oversight remains essential in scenarios where errors could impact critical business decisions.
- Software Engineering Support: Assists with code explanation, documentation creation, and general development tasks, all while ensuring that source code remains within approved, secure environments.
Deployment Strategies and Considerations
- Location and Governance: On-premise installation offers direct oversight of hardware and network connectivity. Conversely, dedicated hosting minimizes the burden of physical infrastructure management but necessitates verification of provider access protocols and data residency.
- Financial and Capacity Planning: Operational costs include hardware acquisition, energy consumption, hosting fees, and maintenance. The required capacity is determined by model size, document length, and concurrent user volume.
- Performance Evaluation: Smaller models typically demand fewer computational resources. These should be rigorously tested against representative tasks to verify that accuracy and response times meet organizational standards.
- Security and Upkeep: A private deployment does not inherently ensure security. Continuous attention must be paid to access controls, activity logging, software updates, and the protection of stored conversation histories.
How NobleConsul Can Assist
- NobleConsul can evaluate specific use cases, compare various deployment architectures, and validate models against specific business requirements.
- The firm can also facilitate the provisioning of server hardware for private LLM setups, selecting appropriate configurations for GPU, memory, storage, and networking based on workload demands and budget constraints.
- Additional support may involve integrating these solutions with existing internal systems and planning for long-term operation, maintenance, and capacity scaling.
FAQ: Private LLM Consulting
When should an organization seek private LLM consulting?
Consulting is advisable when an organization intends to leverage AI with sensitive data—such as internal records, source code, or customer details—without depending on public AI services. It is also beneficial when transitioning an AI pilot to production, ensuring the selection of appropriate models, hardware, and infrastructure.
What hardware is required to host a private LLM?
The primary requirement is GPU memory, which scales with the size of the AI model. A compact model (7–8 billion parameters) can function 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 usage or longer document processing may require additional memory resources.
What are the costs associated with running an on-premise LLM?
Costs are driven by model size, user count, and required response speed. A basic internal AI assistant may operate on a single GPU server, whereas larger models demand more powerful, and consequently more expensive, hardware. Organizations must also account for ongoing expenses such as electricity, cooling, software updates, and maintenance. Piloting with actual workloads is the most effective method for estimating total costs.
How does a private LLM differ from ChatGPT Enterprise?
A private LLM resides on infrastructure under the organization’s direct control, offering superior governance over data storage and processing locations. ChatGPT Enterprise, hosted by OpenAI, processes data on its infrastructure under specific contractual terms. Private deployments often utilize open-weight models like Llama, Mistral, or Qwen, for which the organization assumes responsibility for management, maintenance, and updates.