Private LLMs Consulting Services
A private large language model (LLM) operates within a secure environment where the organization retains full authority over access rights, data management, and deployment strategies. This setup enables the use of internal AI tools powered by proprietary company infrastructure or a dedicated hosting environment.
Key Use Cases for Private LLMs
- Internal Knowledge Discovery: Facilitates employee access to insights within corporate documentation, standard operating procedures, and technical guides. The system ensures that responses adhere to pre-existing document permission hierarchies.
- Document Processing: Streamlines the summarization of reports, extraction of key data, and drafting of content from internal resources. Critical decisions still require human oversight to mitigate the risk of errors impacting business outcomes.
- Software Development: Assists with code interpretation, documentation generation, and development workflows, ensuring that source code remains confined to approved, secure environments.
Deployment Considerations and Trade-offs
- Location and Control: On-premise solutions offer direct oversight of hardware and network connectivity. While dedicated hosting minimizes the burden of physical infrastructure management, it is essential to verify provider access policies and data residency.
- Cost and Capacity: Total operational expenses encompass hardware, energy, hosting fees, and maintenance. The necessary computational capacity is determined by model size, document length, and the volume of concurrent users.
- Performance and Quality: Smaller models typically demand fewer computational resources. It is recommended to evaluate them against representative tasks to verify that their accuracy and latency meet operational standards.
- Security and Maintenance: Private deployment does not inherently ensure security. Robust access controls, comprehensive logging, regular updates, and safeguards for stored conversation data remain critical priorities.
How NobleConsul Can Assist
- NobleConsul provides expertise in evaluating specific use cases, comparing deployment architectures, and testing models against defined business requirements.
- NobleConsul offers the provisioning of server hardware tailored for private LLM deployments, with configurations for GPU, memory, storage, and networking optimized according to workload demands and budget constraints.
- Support services extend to integrating these systems with internal platforms, as well as planning for ongoing operations, maintenance, and future capacity enhancements.
Private LLM Consulting: Frequently Asked Questions
When should an organization consider private LLM consulting?
Organizations should consider this when utilizing AI with sensitive data—such as internal documentation, source code, or customer information—without depending on public AI services. It is particularly beneficial when transitioning an AI pilot project into a production environment, requiring the assessment of appropriate models, hardware, and infrastructure.
What hardware is required to run a private LLM?
GPU memory requirements are directly tied to model size. Smaller models (7–8 billion parameters) can operate on a single GPU with 16–24 GB of memory, whereas larger models (70 billion parameters) necessitate approximately 40 GB or more, often distributed across multiple GPUs. Higher concurrency and longer documents also increase memory demands.
What are the costs associated with running an on-premise LLM?
Expenses vary based on model size, user volume, and performance targets. While smaller models may run on a single GPU server, larger ones require more costly hardware. Recurring costs include electricity, cooling systems, software updates, and general maintenance. Conducting a pilot with real-world workloads is the best way to estimate the total investment.
What distinguishes a private LLM from ChatGPT Enterprise?
A private LLM runs on the organization's own infrastructure, granting full control over data processing locations and methods. In contrast, ChatGPT Enterprise is hosted by OpenAI under specific contractual terms. Private LLMs typically utilize open-source models like Llama, Mistral, or Qwen, which the organization is responsible for managing and maintaining.