Private LLM Consulting Services
A private large language model (LLM) operates within an environment where the organization retains full authority over access management, data handling, and deployment strategies. This setup enables the support of internal AI initiatives using either in-house company infrastructure or a dedicated hosted environment.
Use cases for private LLMs
- Internal knowledge retrieval: Enables employees to locate specific information within company documents, operational procedures, and technical manuals. The system must ensure that responses strictly adhere to existing document permission structures.
- Document processing: Facilitates the summarization of reports, extraction of key data points, and drafting of content from internal sources. Human oversight remains essential in scenarios where inaccuracies could impact critical business decisions.
- Software development: Assists with code explanation, documentation generation, and development tasks, ensuring that all source code remains confined within an approved and secure environment.
Deployment options and associated trade-offs
- Location and control: On-premise deployment offers direct oversight of hardware and network access. In contrast, dedicated hosting reduces the burden of physical infrastructure management but necessitates verification of provider access policies and data residency.
- Cost and capacity: Operating expenses include hardware, electricity, 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 computational resources. It is advisable to evaluate them against representative tasks to confirm that their accuracy and response times meet business needs.
- Security and maintenance: Private deployment alone does not ensure security. Continuous attention must be paid to access controls, logging, software updates, and the protection of stored conversation data.
How NobleConsul can assist
- NobleConsul can assist in evaluating specific use cases, comparing deployment strategies, and testing models against defined business requirements.
- They are also able to provision server hardware for private LLM deployments, selecting GPU, memory, storage, and networking configurations based on workload demands and budget constraints.
- Services may extend to integration with internal systems, as well as 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 aim 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, allowing for the assessment of appropriate models, hardware, and infrastructure.
What hardware is needed to run a private LLM?
GPU memory requirements vary based on the model size. Smaller models (7–8 billion parameters) can operate 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. An increase in concurrent users and document length will also necessitate additional memory.
How much does it cost to run an on-premise LLM?
Costs are influenced by model size, user count, and performance requirements. While smaller models can run on a single GPU server, larger ones demand more costly hardware. Recurring expenses include electricity, cooling, software updates, and maintenance. Implementing a pilot with real-world workloads is recommended to estimate the total investment accurately.
What is the difference between a private LLM and ChatGPT Enterprise?
A private LLM runs on your organization's own infrastructure, providing complete control over how and where data is processed. ChatGPT Enterprise, on the other hand, is hosted by OpenAI under its contractual terms. Private LLMs generally utilize open models such as Llama, Mistral, or Qwen, which are managed and maintained directly by your organization.