Private and Enterprise LLM Deployment

Short answer. A private LLM is a large language model that runs on infrastructure you control, so prompts, documents and outputs never leave your environment. AgenticNex AI selects, deploys, adapts and operates open-weight LLMs on-premise, in a private cloud or air-gapped, with retrieval, access control and monitoring.

What is the business problem?

Regulated organisations cannot send patient records, well data or contracts to public AI services, yet they need the productivity of language models.

Why can conventional approaches be insufficient?

  • Public APIs move sensitive data outside the organisation.
  • Self-hosting without LLMOps leads to unpatched, unmonitored models.
  • Model choice is often made on benchmarks rather than on the organisation's own tasks.

What is our engineering approach?

  1. Benchmark candidate open-weight models on your tasks and hardware.
  2. Deploy with efficient serving, quantisation and autoscaling.
  3. Add retrieval, guardrails and role-based access.
  4. Operate with evaluation, patching and usage monitoring.

What does the architecture look like?

Users & applicationsAPI gateway + identityGuardrails & PII filterPrivate LLM serving (GPU)Retrieval over internal dataLogs, evaluation & audit
Private LLM reference architecture. Users & applications → API gateway + identity → Guardrails & PII filter → Private LLM serving (GPU) → Retrieval over internal data → Logs, evaluation & audit

Which technology capabilities are involved?

  • Open-weight LLMs (Llama-, Mistral-, Qwen-class)
  • vLLM / TGI-class serving
  • Quantisation and GPU sizing
  • Fine-tuning and adapters (LoRA)
  • Air-gapped deployment
  • LLMOps monitoring

Example use cases

  • Medical documentation and EMR summarisation
  • Engineering knowledge assistants on sensitive data
  • Government and defence document processing
  • Contract analysis under confidentiality

Expected business and technical outcomes

  • Data stays within your boundary
  • Predictable cost at high usage
  • Model behaviour you can audit
  • [Insert verified project result]

Relevant case studies

Case study 03

Healthcare — Private Medical AI

Case study details coming soon

Frequently asked questions

Is a private LLM as capable as public models?

For many enterprise tasks grounded in internal documents, well-chosen open-weight models perform well. We benchmark on your own tasks before recommending one.

What hardware does a private LLM need?

It depends on model size, concurrency and latency. We size GPU requirements during the benchmarking phase.

Related insights

Digital Transformation

Private LLM vs Public LLM for Enterprise AI

A public LLM runs as a vendor's hosted service; a private LLM runs on infrastructure you control.

Request a Technical Discussion

Discuss private llm for your environment with an AgenticNex engineer.

Request a Technical Discussion