Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organisation retains full authority over access rights, data processing, and deployment strategies. This setup enables the integration of internal AI tools by leveraging either the company’s own infrastructure or a dedicated hosted solution.
Applications for private LLMs
- Internal knowledge retrieval: Assists employees in locating relevant information within corporate documents, operational procedures, and technical manuals, ensuring that responses adhere to existing document permissions.
- Document processing: Facilitates the summarisation of reports, extraction of key insights, and drafting of initial content from internal resources. Human oversight remains critical in scenarios where errors could impact business decisions.
- Software development: Aids in code explanation, documentation, and development tasks while ensuring that source code remains secured within an approved environment.
Deployment options and trade-offs
- Location and control: On-premise deployment offers direct oversight of hardware and network access. Dedicated hosting reduces the burden of physical infrastructure management but necessitates verification of provider access rights and data residency.
- Cost and capacity: Operational expenses include hardware, power, hosting, and maintenance. The required capacity is determined by model size, document length, and the number of concurrent users.
- Performance and quality: Smaller models typically demand fewer resources. Testing should be conducted against representative tasks to verify that accuracy and response times meet business needs.
- Security and maintenance: Private deployment does not inherently guarantee security. Attention must still be paid to access controls, logging, updates, and the protection of stored conversations.
How NobleConsul can support
- NobleConsul can assist in evaluating use cases, comparing deployment strategies, and testing models against specific business requirements.
- NobleConsul is also equipped to provision server hardware for private LLM deployments, tailoring GPU, memory, storage, and networking configurations to the workload and budget.
- Support services may encompass integration with internal systems, as well as planning for ongoing operations, maintenance, and capacity scaling.
Frequently Asked Questions on Private LLM Consulting
When should an organisation consider private LLM consulting?
Organisations should consider this when they intend to utilise AI with 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 and determining the appropriate models, hardware, and infrastructure.
What hardware is required to run a private LLM?
GPU memory needs vary by 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) require approximately 40 GB or more, often distributed across multiple GPUs. Higher concurrency and longer documents also necessitate additional memory.
What is the cost of running an on-premise LLM?
Expenses are influenced by model size, user count, and performance demands. Smaller models can run on a single GPU server, while larger ones require more costly hardware. Recurring costs include electricity, cooling, software updates, and maintenance. A pilot using real-world workloads aids in estimating the total investment.
What distinguishes a private LLM from ChatGPT Enterprise?
A private LLM runs on the organisation's own infrastructure, granting control over data processing methods and location. ChatGPT Enterprise is hosted by OpenAI under its contractual terms. Private LLMs typically employ open models such as Llama, Mistral, or Qwen, which are managed and maintained by the organisation.