Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organization retains full authority over access management, data handling, and deployment. This setup enables the support of internal AI tools by leveraging either the company’s own infrastructure or a dedicated hosted environment.
Use cases for private LLMs
- Internal knowledge retrieval: Facilitates employee access to insights from company documents, operational procedures, and technical manuals. Responses are configured to honor established document permission levels.
- Document management: Enables the summarization of reports, extraction of key details, and drafting of content from internal materials. Human oversight remains essential in scenarios where errors could impact critical business decisions.
- Software engineering: Assists with code interpretation, documentation, and development workflows while ensuring that source code remains confined within approved security boundaries.
Deployment strategies and trade-offs
- Location and governance: On-premise installations offer direct control over hardware and network connectivity. In contrast, dedicated hosting alleviates the burden of physical infrastructure management, though it necessitates verification of provider access policies and data residency.
- Expenditure and scalability: Operational expenses include hardware, power consumption, hosting fees, and maintenance. The necessary capacity is determined by factors such as model size, document length, and the number of concurrent users.
- Performance and accuracy: Smaller models typically demand fewer computational resources. It is advisable to evaluate these against representative tasks to confirm that accuracy and response latency meet organizational standards.
- Security and upkeep: Private deployment alone does not ensure a secure posture. Continuous attention must be paid to access controls, audit logging, system updates, and the protection of stored conversation data.
How NobleConsul can assist
- NobleConsul supports the assessment of use cases, comparison of deployment alternatives, and validation of models against specific business requirements.
- NobleConsul also handles the provisioning of server hardware for private LLM deployments, offering tailored configurations for GPU, memory, storage, and networking based on workload demands and budgetary constraints.
- Additional support encompasses integration with internal systems as well as the planning of ongoing operations, maintenance, and capacity upgrades.
Private LLM Consulting Frequently Asked Questions
When should an organization consider private LLM consulting?
Consulting is recommended when an organization aims to utilize AI on sensitive data, such as internal documents, source code, or customer information, without depending on public AI services. It is also beneficial when transitioning an AI pilot to production, requiring the assessment of appropriate models, hardware, and infrastructure.
What hardware is required to run a private LLM?
The primary hardware requirement is GPU memory, which is dictated by the size of the AI model. A smaller model (7–8 billion parameters) can operate on a single GPU equipped with 16–24 GB of memory. Conversely, a larger model (70 billion parameters) typically requires approximately 40 GB or more, often necessitating multiple GPUs. If concurrent user loads increase or longer documents are processed, additional memory capacity may be required.
What is the cost of running an on-premise LLM?
Costs are influenced by model size, user volume, and required response speeds. A basic internal AI assistant may function on a single GPU server, whereas larger models demand more powerful and costly hardware. Organizations must also account for recurring expenses, including electricity, cooling, software updates, and maintenance. Conducting a pilot with realistic workloads is the most effective method for estimating total costs.
What is the difference between a private LLM and ChatGPT Enterprise?
A private LLM operates on infrastructure governed by your organization, providing enhanced control over data storage and processing locations. ChatGPT Enterprise, hosted by OpenAI, processes data on their infrastructure under their specific contractual terms. Private LLMs frequently utilize open models such as Llama, Mistral, or Qwen, for which your organization assumes responsibility for management, maintenance, and updates.