Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organization retains full authority over access protocols, data management, and deployment strategies. This approach enables the development of internal AI tools powered by company infrastructure or a dedicated hosted setup.
Key areas for private LLM implementation
- Internal knowledge retrieval: Facilitate employee access to critical information found in company documentation, operational procedures, and technical manuals, ensuring that responses adhere to established document permissions.
- Document handling: Streamline the summarization of reports, extraction of key data, and drafting of content from internal resources, while maintaining a mandatory human review process for decisions with potential business impact.
- Software engineering: Enhance code explanation, documentation, and development workflows, all while ensuring source code remains secured within an approved environment.
Deployment strategies and considerations
- Location and governance: On-premise deployment offers direct oversight of hardware and network access. Alternatively, dedicated hosting minimizes the burden of physical infrastructure management, though it requires verification of provider access rights and data residency.
- Cost and scaling: Operational expenses are driven by hardware, energy, hosting, and maintenance. Determining the necessary capacity involves evaluating model size, document length, and concurrent user load.
- Performance and fidelity: While smaller models typically demand fewer resources, they should be rigorously tested against representative tasks to confirm that accuracy and response times meet business standards.
- Security and upkeep: A private deployment does not inherently ensure security. Continuous attention is required for access controls, logging, software updates, and the protection of stored conversation data.
How NobleConsul can assist
- NobleConsul can facilitate the assessment of use cases, comparison of deployment options, and testing of models against specific business requirements.
- The team can also provision server hardware for private LLM setups, offering tailored GPU, memory, storage, and networking configurations based on workload demands and budget constraints.
- Ongoing support encompasses integration with internal systems and strategic planning for continuous operation, maintenance, and capacity enhancements.
Private LLM Consulting Frequently Asked Questions
When should an organisation consider private LLM consulting?
Organizations should consider this when aiming to leverage AI for sensitive data, such as internal documents, source code, or customer information, without depending on public AI services. It is also pivotal when transitioning an AI pilot to production and determining the optimal models, hardware, and infrastructure.
What hardware is needed to run a private LLM?
The primary requirement is GPUs with sufficient memory to accommodate the model. A smaller model (7–8 billion parameters) can operate on a single GPU with 16–24 GB of memory. In contrast, a large model (70 billion parameters) necessitates 40 GB or more, typically distributed across multiple GPUs. Higher user concurrency and longer documents will require increased capacity.
How much does it cost to run an on-premise LLM?
Expenses vary based on model size, user count, and performance expectations. Smaller models may run on a single GPU server, whereas larger ones demand more costly hardware. Recurring costs include electricity, cooling, software updates, and maintenance. A pilot using real-world workloads is recommended to estimate the total investment.
What is the difference between a private LLM and ChatGPT Enterprise?
A private LLM functions on your organization's own infrastructure, providing control over data processing locations and methods. ChatGPT Enterprise, hosted by OpenAI, operates under specific contractual terms. Private LLMs often utilize open-source models like Llama, Mistral, or Qwen, which your organization directly manages and maintains.
Can you see a prototype before committing?
Yes. Following an initial consultation regarding your needs and current systems, NobleConsul constructs a prototype focused on a real task, such as internal document searching. This is accompanied by a proposal detailing implementation costs, total cost of ownership, and training requirements.
Who owns the model, hardware and data afterwards?
Your organization retains full ownership. NobleConsul transfers the hardware, code, credentials, and documentation, subsequently revoking its own access. Any future support is conducted only with your explicit approval.