Private LLM Consulting Services
A private large language model (LLM) runs in an environment where an organization controls access, data handling, and deployment. It can support internal AI tools using company infrastructure or a dedicated hosted environment.
Where private LLMs are useful
- Internal knowledge search: Help employees find information in company documents, procedures, and technical manuals. Answers should respect existing document permissions.
- Document processing: Summarize reports, extract information, and prepare drafts from internal material. Human review remains necessary where mistakes could affect business decisions.
- Software development: Support code explanation, documentation, and development tasks while keeping source code within an approved environment.
Deployment choices and trade-offs
- Location and control: On-premise deployment provides direct control over hardware and network access. Dedicated hosting reduces physical infrastructure work but requires checking provider access and data location.
- Cost and capacity: Hardware, electricity, hosting, and maintenance contribute to operating costs. Required capacity depends on model size, document length, and simultaneous users.
- Performance and quality: Smaller models generally need fewer resources. Test them against representative tasks to determine whether their accuracy and response times are sufficient.
- Security and maintenance: Private deployment alone does not guarantee security. Access controls, logging, updates, and protection of stored conversations still need attention.
How NobleConsul can help
- NobleConsul could help assess use cases, compare deployment options, and test models against business requirements.
- NobleConsul can also provision server hardware for private LLM deployments, with GPU, memory, storage, and networking choices based on workload and budget.
- Support could include integration with internal systems and planning ongoing operation, maintenance, and capacity upgrades.
Private LLM Consulting Frequently Asked Questions
When should an organisation consider private LLM consulting?
When it wants to use AI with sensitive data, such as internal documents, source code or customer information, without relying on public AI services. It is also useful when moving an AI pilot into production and assessing the right models, hardware and infrastructure
What hardware is needed to run a private LLM?
Mainly GPUs with enough memory for the model. A small model (7–8 billion parameters) runs on one GPU with 16–24 GB of memory. A large model (70 billion parameters) needs 40 GB or more, usually across several GPUs. More users and longer documents need more capacity.
How much does it cost to run an on-premise LLM?
Costs depend on model size, number of users and performance requirements. Smaller models can run on a single GPU server, while larger ones need more expensive hardware. Ongoing costs include electricity, cooling, software updates and maintenance. A pilot using real workloads helps estimate 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 how and where data is processed. ChatGPT Enterprise is hosted by OpenAI under its contractual terms. Private LLMs typically use open models such as Llama, Mistral or Qwen, which your organisation manages and maintains.
Can you see a prototype before committing?
Yes. After a first call about your needs and current systems, NobleConsul builds a prototype on a real task, such as searching internal documents. It comes with a proposal showing implementation cost, total cost of ownership and training.
Who owns the model, hardware and data afterwards?
Your organisation. NobleConsul hands over the hardware, code, credentials and documentation, then removes its own access. Any later support uses access only with your approval.