Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organization retains full authority over access, data handling, and deployment strategies. This setup supports internal AI tools by leveraging either the company's own infrastructure or a dedicated hosted environment tailored to specific needs.
Where private LLMs are useful
- Internal knowledge search: This capability helps employees locate relevant information across company documents, standard operating procedures, and technical manuals. Crucially, the system must respect and enforce existing document permission structures.
- Document processing: It can automate the summarization of reports, extraction of key data points, and drafting of content from internal materials. However, human oversight remains essential in scenarios where errors could have significant implications for business decisions.
- Software development: It supports code explanation, documentation generation, and various development tasks, ensuring that source code remains secure within an approved and controlled environment.
Deployment choices and trade-offs
- Location and control: On-premise deployment offers direct, granular control over hardware and network access. In contrast, dedicated hosting reduces the burden of managing physical infrastructure, though it necessitates thorough verification of the provider's access protocols and data residency locations.
- Cost and capacity: Operating costs are influenced by hardware, electricity, hosting fees, and maintenance. The required capacity is determined by the model's size, the length of processed documents, and the number of simultaneous users.
- Performance and quality: Smaller models generally demand fewer computational resources. It is advisable to test these models against representative tasks to verify that their accuracy and response times meet business standards.
- Security and maintenance: Private deployment alone does not ensure security. Continued attention must be paid to access controls, logging, software updates, and the protection of stored conversations.
How NobleConsul can help
- NobleConsul can assist in evaluating potential use cases, comparing different deployment options, and testing models against specific business requirements.
- NobleConsul also offers the provisioning of server hardware for private LLM deployments, selecting GPU, memory, storage, and networking configurations based on the specific workload and budget constraints.
- Support services may include integration with existing internal systems, as well as strategic planning for ongoing operations, maintenance, and capacity upgrades.
Private LLM Consulting Frequently Asked Questions
When should an organisation consider private LLM consulting?
Organizations should consider this when they need to leverage AI with sensitive data, such as internal documents, source code, or customer information, without depending on public AI services. It is also highly relevant when transitioning an AI pilot project into a production environment and assessing the appropriate models, hardware, and infrastructure.
What hardware is needed to run a private LLM?
GPU memory requirements are directly tied to the model's size. Smaller models, typically with 7–8 billion parameters, can operate on a single GPU equipped with 16–24 GB of memory. In contrast, larger models with 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 the model size, the number of users, and specific performance requirements. While smaller models can run on a single GPU server, larger models demand more expensive hardware infrastructure. Ongoing expenses include electricity, cooling systems, software updates, and routine maintenance. Implementing a pilot using real-world workloads is the best way to estimate the total investment accurately.
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 how and where data is processed. ChatGPT Enterprise, on the other hand, is hosted by OpenAI under their specific contractual terms. Private LLM implementations typically utilize open-source models such as Llama, Mistral, or Qwen, which the organization manages and maintains directly.