Dedicated LLM Solutions Consulting Services
A dedicated large language model (LLM) operates within an environment where your organisation retains full authority over access rights, data management, and deployment. This approach supports internal AI initiatives by leveraging your own infrastructure or a dedicated hosting environment tailored to your needs.
Key benefits of dedicated LLMs
- Internal knowledge retrieval: Enables staff to locate relevant information within company documents, standard operating procedures, and technical manuals, ensuring that answers strictly adhere to existing document access controls.
- Document automation: Facilitates the summarisation of reports, extraction of key data, and drafting of content from internal sources. It is crucial to retain human oversight for tasks where errors could impact critical business decisions.
- Software development: Assists with code explanation, documentation generation, and development workflows, all while keeping source code secure within an approved environment.
Deployment options and considerations
- Location and control: On-premise deployments offer direct oversight of hardware and network access. While dedicated hosting reduces the burden of physical infrastructure management, it is essential to verify provider access policies and data residency locations.
- Cost and capacity: Operational expenses include hardware, power, hosting, and maintenance. The required capacity is determined by model size, the length of documents processed, and the number of concurrent users.
- Performance and quality: Smaller models typically demand fewer resources. It is recommended to test them against representative tasks to ensure their accuracy and response times meet your standards.
- Security and maintenance: A private deployment does not inherently guarantee security. You must still implement robust access controls, logging, updates, and protections for stored conversations.
How NobleConsul can assist
- NobleConsul can help evaluate use cases, compare deployment strategies, and validate models against your specific business requirements.
- NobleConsul is also able to provision server hardware for dedicated LLM deployments, selecting GPU, memory, storage, and networking configurations based on your workload and budget.
- Support services can encompass integration with internal systems as well as planning for ongoing operations, maintenance, and future capacity upgrades.
Frequently Asked Questions about Private LLM Consulting
When should an organisation consider private LLM consulting?
Consider this when you wish to utilise AI with sensitive data—such as internal documents, source code, or customer information—without relying 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 needed to run a private LLM?
GPU memory requirements vary with model size. Smaller models (7–8 billion parameters) can run on a single GPU with 16–24 GB of memory, whereas larger models (70 billion parameters) typically require around 40 GB or more, often distributed across multiple GPUs. Higher concurrency and longer documents will necessitate additional memory.
What is the cost of running an on-premise LLM?
Costs are influenced by model size, user count, and performance demands. Smaller models can operate on a single GPU server, while larger ones require more expensive hardware. Ongoing expenses include electricity, cooling, software updates, and maintenance. Conducting a pilot with real-world workloads helps in estimating the total investment.
What is the difference between a private LLM and ChatGPT Enterprise?
A private LLM runs on your organisation's own infrastructure, giving you control over where and how data is processed. ChatGPT Enterprise is hosted by OpenAI under its contractual terms. Private LLMs typically utilise open-source models such as Llama, Mistral, or Qwen, which your organisation is responsible for managing and maintaining.