Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organization retains full oversight of access, data management, and deployment strategies. This approach supports internal AI initiatives by leveraging company infrastructure or a dedicated hosted environment.
Key applications for private LLMs
- Internal knowledge retrieval: Enables employees to efficiently locate information within corporate documents, operational procedures, and technical manuals. Crucially, the system must respect existing document permission structures.
- Document processing: Facilitates the summarization of reports, extraction of key data, and drafting of content from internal materials. Human oversight remains essential in scenarios where errors could impact critical business decisions.
- Software development: Assists with code explanation, documentation generation, and development tasks, ensuring that source code remains securely within an approved environment.
Deployment options and associated trade-offs
- Location and control: On-premise deployment offers direct command over hardware and network access. Alternatively, dedicated hosting reduces the burden of physical infrastructure management but requires thorough verification of provider access and data residency.
- Cost and capacity: Operational expenses are driven by hardware, energy, 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. It is advisable to benchmark them against representative tasks to verify that accuracy and response times meet business needs.
- Security and maintenance: Private deployment does not inherently ensure security. Robust access controls, logging, regular updates, and the protection of stored conversations must still be actively managed.
How NobleConsul can assist
- NobleConsul can assist in evaluating use cases, comparing deployment strategies, and testing models against specific business requirements.
- NobleConsul also provides server hardware provisioning for private LLM deployments, offering tailored configurations for GPU, memory, storage, and networking based on workload demands and budget constraints.
- Support services include integration with internal systems, as well as strategic planning for ongoing operations, maintenance, and capacity expansion.
Private LLM Consulting Frequently Asked Questions
When should an organization consider private LLM consulting?
This is particularly relevant when an organization intends to utilize 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?
Primarily, GPUs with sufficient memory for the model. A smaller model (7–8 billion parameters) can operate on a single GPU with 16–24 GB of memory. In contrast, a larger model (70 billion parameters) requires 40 GB or more, typically distributed across multiple GPUs. Higher user counts and longer documents necessitate greater capacity.
What is the cost of running an on-premise LLM?
Expenses vary based on model size, user count, and performance requirements. Smaller models may run on a single GPU server, whereas larger ones demand more expensive hardware. Recurring costs include electricity, cooling, software updates, and maintenance. Conducting a pilot with real workloads is effective for estimating the total investment.
What distinguishes a private LLM from ChatGPT Enterprise?
A private LLM runs on the organization’s own infrastructure, granting control over how and where data is processed. ChatGPT Enterprise, however, is hosted by OpenAI under its contractual terms. Private LLMs typically utilize open-source models like Llama, Mistral, or Qwen, which the organization manages and maintains directly.
Is it possible to review a prototype before committing?
Yes. Following an initial discussion regarding your needs and current systems, NobleConsul develops a prototype focused on a real task, such as searching internal documents. This is accompanied by a proposal detailing implementation costs, total cost of ownership, and training requirements.
Who retains ownership of the model, hardware, and data afterwards?
Your organization. NobleConsul transfers the hardware, code, credentials, and documentation, then removes its own access rights. Any subsequent support is provided only with your approval and uses access strictly within those parameters.