Private LLM Deployment for Healthcare: Running Models Inside Your Hospital

Short answer: a private LLM is a language model whose weights and inference run on infrastructure you control. For a hospital, the strictest version is on-premise: an open-weight model in the 7–14B range on a GPU server inside your network, with no external model API, every request logged, and staff approving every output. Gofar Labs installs and tunes that stack; the core platform is a working prototype available for pilot evaluation.

What does “private LLM” actually mean?

Vendors use the word for very different setups. The question that separates them: does the product still work with all outbound traffic blocked?

Setup	Where the model runs	Who operates it	PHI leaves your network?	Works with egress blocked?
Public AI API with enterprise terms	Provider’s shared cloud	Provider	Yes, under a BAA	No
Dedicated or private cloud tenancy	Vendor-hosted, single-tenant	Vendor	Yes, under a BAA	No
Self-hosted in your cloud account	Your cloud subscription	You or a vendor	Into your cloud tenancy	Depends on design
On-premise	Your data center	You, with vendor support	No	Yes

All four can be appropriate. This guide covers the last one. For a vendor-by-vendor framing, see our on-premise vs cloud healthcare AI buyer’s guide.

How a private LLM fits inside a hospital network

Your hospital network

1. 01Staff & systemsReview queues, document feeds, EHR exports, MCP-enabled systems
2. 02Private LLM on your GPUInference server, model manager, access control, audit log
3. 03Draft outputSummaries, extracted fields, letters, code suggestions
4. 04Human reviewStaff approve, edit, or reject every draft

× Public AI APIs Not part of the data path. The platform never phones home.

Choosing the model

• Open weights. You hold the model file, so it runs without a vendor’s API and can be audited and replaced. Families such as Llama, Qwen, Gemma, Phi, and Mistral are common starting points.
• Small and specialized. For document-heavy workflows, 7–14B models tuned for one task are the practical sweet spot on a single server.
• Quantized. Compressed weights let an 8–14B model fit a 24GB GPU with modest quality loss; the trade-off is validated on your own documents.
• Licensed for your use. Open-weight licenses vary by family and version. Review the license before deployment.
• Tuned with adapters. Fine-tuning trains a small adapter on hospital hardware while the base model stays frozen, so you can swap or roll back without retraining everything.

Sizing the hardware

Deployment	Typical hardware	Fits
Single workflow	One server with a 24GB GPU	Document processing and drafting with a quantized 8–14B model
Several departments	Multi-GPU server	Multiple tuned models, with department isolation between them
Existing hardware	Your own GPU server, certified first	Avoids a new purchase if the server meets the requirements

Speed is not the selling point. Hospitals accept slightly slower processing in exchange for data that never leaves the premises. See the full on-premise stack for how hardware, platform, models, tools, and the MCP gateway fit together.

The software around the model

Running a model is the easy part. What makes it deployable in a hospital is everything around it:

• Inference server — vLLM or llama.cpp, serving the model to internal applications.
• Model manager — swap in better open weights as they ship, with rollback.
• Audit logging — every inference, prompt, and completion recorded with actor, timestamp, and data scope, streaming to your SIEM.
• Role-based access — mapped to your directory where possible.
• Encryption at rest — keys stay in your infrastructure.
• Router — a small classifier that sends each request to the right specialized model.
• Updates — signed offline packages applied on your schedule, so the subnet can stay behind a strict egress firewall.

Governance your security team will ask about

1. Can the deployment run with all outbound traffic blocked?
2. What exactly is logged, where, and for how long?
3. Who can query which model, and with what data?
4. How are behavioral-health data kept separate? (We support department isolation for 42 CFR Part 2.)
5. How are new model versions evaluated before replacing the current one?
6. How do outside AI agents reach the model? (Through a policy-enforcing MCP gateway, with every call logged.)
7. Who on the vendor side can access PHI, and under what agreement?

On the last point, see Can a hospital run an LLM without a BAA?.

When a private LLM is the wrong choice

• You need frontier-scale reasoning across open-ended topics, not repeated workflows.
• There is no one to own the server — on-premise hardware needs a home in your data center and an IT contact.
• Volumes are tiny and the data is not sensitive enough to justify dedicated hardware.

In those cases a cloud service under a Business Associate Agreement may be the better fit. We would rather tell you that on the first call.

Build it yourself or buy a platform?

Everything above can be assembled from open-source parts, and some hospital IT teams do. The work is in the parts that are not the model: access control, audit trails that satisfy compliance, review interfaces for staff, update processes that work behind an egress firewall, and evaluation on your own documents. Gofar Labs packages those pieces as an installable platform and stays involved through tuning and rollout. We also plan to publish parts of it openly.

Where Gofar Labs stands today

Working prototype

The core platform — local inference, model management, audit logging, fine-tuning, and the MCP gateway — is a working prototype available for hospital pilot evaluation. Most hospitals start with one workflow, usually document processing.

The platform · Fine-tuning · Security · Workflow tools

Frequently asked questions

What is a private LLM in healthcare?

A large language model whose weights and inference run on infrastructure the healthcare organization controls, so prompts and patient data are not sent to a public AI API. For hospitals, the strictest form is an on-premise deployment on a GPU server inside the hospital network.

Can a hospital run its own LLM?

Yes. Open-weight models in the 7–14B parameter range run on a single hospital GPU server. For document workflows, one 24GB GPU runs a quantized 8–14B model well. The work is less about the model and more about the surrounding platform: access control, audit logging, updates, and human review.

Which open-weight models are used for private healthcare LLMs?

Common choices come from families such as Llama, Qwen, Gemma, Phi, and Mistral. Licenses differ between families and versions, so check each model’s license for your intended use. Models can be swapped as better open weights are released.

Is a private LLM HIPAA compliant?

No deployment is compliant by itself; compliance is confirmed through your own review of a specific deployment. An on-premise LLM supports that review by keeping inference inside your network, encrypting data at rest, enforcing role-based access, and logging every request. We describe our stack as HIPAA-friendly by architecture.

Is a private LLM as capable as the largest cloud models?

Not for open-ended reasoning across every topic. For narrow, repeated hospital workflows — reading documents, extracting fields, drafting from templates — a smaller model tuned for that task can perform well, and it keeps the data in your building.

Does a private LLM need a Business Associate Agreement?

If the model runs on your hardware and no vendor accesses PHI, the model itself creates no disclosure. A BAA is still appropriate for any vendor staff who may work with PHI, for example during installation or support.