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 setup supports the integration of internal AI tools, whether they are hosted on the company’s own infrastructure or within a dedicated, isolated environment.
Use cases for private LLMs
- Internal knowledge retrieval: Facilitates employee access to specific information within company documents, standard operating procedures, and technical manuals. The system ensures that responses strictly adhere to existing document permission settings.
- Document analysis: Automates the summarization of reports, extraction of key data points, and drafting of content from internal materials. Human oversight remains critical to verify accuracy, particularly where errors could impact strategic business decisions.
- Software engineering: Assists with code explanation, documentation generation, and various development tasks, ensuring that source code remains securely contained within an approved, controlled environment.
Deployment strategies and considerations
- Location and oversight: On-premise deployment offers direct control over hardware and network boundaries. While dedicated hosting reduces the burden of physical infrastructure management, it necessitates thorough verification of the provider’s access protocols and data residency locations.
- Expenditure and scaling: Operational costs encompass hardware, energy consumption, hosting services, and ongoing maintenance. The required computing capacity is dictated by the size of the model, the length of processed documents, and the number of simultaneous users.
- Efficiency and output quality: Compact models typically demand fewer computational resources. It is essential to benchmark these models against representative real-world tasks to confirm that their accuracy and response speeds meet business standards.
- Security and upkeep: Deploying a private model does not inherently ensure a secure environment. 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 assist in evaluating specific use cases, comparing different deployment architectures, and testing model performance against defined business requirements.
- NobleConsul offers server hardware provisioning for private LLM deployments, allowing for tailored selections of GPU, memory, storage, and networking capabilities based on workload demands and budget constraints.
- Support services may include the integration of LLMs with existing internal systems, as well as the strategic planning of ongoing operations, maintenance, and future capacity enhancements.
Private LLM Consulting Frequently Asked Questions
When should an organisation consider private LLM consulting?
Consulting is advisable when an organization seeks to utilize AI for handling sensitive data—such as internal documentation, source code, or customer records—without depending on public AI services. It is also beneficial when transitioning an AI pilot project into production and determining the optimal models, hardware, and infrastructure requirements.
What hardware is needed to run a private LLM?
GPU memory requirements are directly linked to 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) generally require around 40 GB or more, often distributed across multiple GPUs. An increase in concurrent users or document length will also necessitate additional memory capacity.
How much does it cost to run an on-premise LLM?
Costs vary based on model size, user volume, and performance expectations. Smaller models may run on a single GPU server, while larger models require more substantial hardware investments. Ongoing expenses include electricity, cooling systems, software updates, and maintenance. Conducting a pilot with representative workloads is the best method for estimating the total investment required.
What is the difference between a private LLM and ChatGPT Enterprise?
A private LLM runs on the organization’s own infrastructure, providing full control over data processing methods and locations. In contrast, ChatGPT Enterprise is hosted by OpenAI under specific contractual terms. Private LLMs typically utilize open-source models such as Llama, Mistral, or Qwen, which the organization is responsible for managing and maintaining.