Local LLM Setup for Private Company AI
Run private models on your own infrastructure with a company AI portal, SSO, role access, RAG over internal documents, model routing, token quotas and audit logs.
Last updated: 2026-07-16. Provider: SnapSiteBuild AI Centre of Excellence — founder Krish Chimakurthy.
Bottom line up front: a local LLM setup runs large language models on hardware you control — your own server, data centre or isolated cloud tenancy — so prompts, internal documents and AI answers never leave your boundary. SnapSiteBuild designs, deploys and governs that private AI infrastructure end to end, then puts a single secure gateway in front of it for sign-in, access control, model routing, token quotas and audit logs.
TL;DR. A production self-hosted LLM is more than a model on a GPU. It needs a company AI portal, identity and role-based access, retrieval over your own documents, routing between models, usage quotas and a full audit trail. Here is what a private LLM setup includes, how on-premise AI compares to private-cloud and external APIs, and the exact open stack we run — including our own AMD ROCm GPU node.
- Private by design — data stays inside your network; no third-party vendor sees your prompts or files.
- No per-token vendor billing for local usage — you run open models on hardware you own instead of paying a metered API for every call.
- Governed access — SSO, role permissions, per-team token quotas and audit logs via an enterprise AI gateway.
- Grounded answers — a RAG knowledge base over your internal documents, with permissions that mirror the source files.
What is a local LLM setup?
A local LLM setup is a self-hosted large language model that your company runs on its own infrastructure rather than calling a public AI service over the internet. The model weights, the inference server, the document index and the conversation logs all sit inside your perimeter. Employees and applications reach the model through an internal endpoint — a browser-based company AI portal or an OpenAI-compatible API — secured by your existing identity provider.
This matters most for organisations handling regulated, confidential or competitive data: patient records, financial statements, source code, contracts, customer PII or proprietary research. With on-premise AI, sensitive content is never transmitted to an outside model provider, so you remove an entire category of data-leakage and vendor-lock-in risk while keeping full control over which model versions you run and when they change.
Local LLM vs private cloud vs external API
Most companies do not choose one option forever — they route between them. The table below compares the three approaches so you can decide where each workload belongs.
| Dimension |
Local / on-premise LLM |
Private-cloud LLM |
External AI API |
| Where your data goes |
Stays on hardware you own; nothing leaves the building or VPC |
Inside your isolated cloud tenancy |
Sent to a third-party vendor's servers |
| Cost model |
No per-token vendor billing for local usage; you fund hardware and power |
Hourly GPU / instance rental |
Pay per token on every call, indefinitely |
| Control over the model |
Full — pin versions, fine-tune, run fully offline |
High, within the provider's catalogue |
Vendor controls the model and forces updates |
| Latency |
Predictable on the local network |
Low within the same region |
Depends on vendor load and the public internet |
| Best for |
Sensitive data, steady high volume, regulated workloads |
Burst capacity with no on-site hardware |
Frontier-model quality for spiky, low-volume tasks |
| Setup effort |
Higher upfront (we handle it) |
Medium |
Lowest |
A practical architecture uses a local model for the bulk of everyday, sensitive work and keeps a governed external model as a fallback for the hardest queries — with the gateway deciding which model handles each request. That hybrid keeps data private by default without giving up frontier-model quality when you genuinely need it.
What's inside a production private LLM setup
A model serving tokens is the easy part. The components below are what turn it into company AI infrastructure that security, finance and IT will actually approve.
- Company AI portal
- A branded internal chat workspace where staff use approved models, saved prompts and internal knowledge — no personal accounts, no shadow AI.
- SSO and role-based access
- Sign-in through your existing SAML or OIDC identity provider. Roles decide which models, document collections and admin functions each person can reach.
- RAG over internal documents
- Retrieval-augmented generation grounds answers in your own files, so the model cites real sources instead of guessing. Retrieval permissions mirror the underlying document access. See our RAG knowledge base service.
- Model routing
- One endpoint, many models. Route a fast small model for everyday chat, a larger local model for hard reasoning, and an optional external model for edge cases — all controlled centrally.
- Token quotas and cost visibility
- Per-user and per-team quotas protect shared GPU capacity and give finance internal showback, even though there is no per-token vendor billing for local usage.
- Audit logs
- Every request records who asked, when, which model answered, how many tokens were used and which documents were retrieved — ready for compliance review and incident response.
- Admin dashboard
- Live backend health, usage by team, quota status, the model catalogue and searchable audit history in one operations view.
SnapSiteBuild delivers these as a single platform: the private model node plus the enterprise AI gateway that handles authentication, the prompt firewall, routing, quotas, audit logging and the cost dashboard.
The self-hosted LLM stack we deploy
We build on proven open infrastructure rather than a black box, so you keep full ownership and can run it without us if you ever choose to.
- Inference engines: Ollama for fast, simple deployment of open models; vLLM for high-throughput, concurrent production serving; and llama.cpp for efficient quantised models on constrained or CPU-only hardware.
- Vector search: Qdrant or pgvector (Postgres) to store embeddings for retrieval over your documents.
- Packaging: Docker containers and reproducible configuration so the same setup runs on a workstation, an on-prem server or your cloud.
- Gateway layer: an OpenAI-compatible API so your existing tools, SDKs and editors connect with a single base-URL change.
This is not theory for us. SnapSiteBuild runs its own AMD ROCm GPU node serving an OpenAI-compatible endpoint — the same architecture we deploy for clients. You can connect to it directly through our hosted local LLM developer access to test the model and the gateway before committing to dedicated hardware.
How a local LLM deployment rolls out
- Assess: map use cases, data sensitivity, expected volume and which models and hardware (GPU, VRAM, on-prem vs cloud) fit.
- Prepare data: clean and structure your documents for retrieval — our AI data migration service turns messy PDFs, spreadsheets and databases into RAG-ready knowledge.
- Deploy: stand up the inference engine, vector store and gateway in Docker, wired to your identity provider.
- Govern: configure roles, token quotas, model routing, the prompt firewall and audit logging.
- Test: evaluate accuracy, grounding, prompt-injection resistance and data-leakage with our AI testing and QA process before anyone relies on it.
- Operate: hand over the admin dashboard, runbooks and monitoring, with ongoing support as usage grows.
Why trust SnapSiteBuild for on-premise AI
SnapSiteBuild is an AI Centre of Excellence founded by Krish Chimakurthy, pairing nearly two decades of IT delivery, software testing and project management with modern AI engineering. The difference from a generic install: we ship the governance layer — SSO, quotas, routing, audit and testing — not just a model on a port, and we run this exact stack ourselves on our own AMD GPU hardware. There are no per-seat lock-ins to our platform: the deployment is built on open components you fully own.
Local LLM setup FAQ
- What is a local LLM setup?
- A self-hosted large language model running on hardware you control — on-premise or in a private cloud — reached through a gateway that adds authentication, quotas and audit logs, so sensitive data never leaves your environment.
- How is a local LLM different from cloud AI APIs?
- Local LLMs keep data inside your network, carry no per-token vendor billing for local usage and give you full control over model versions. External APIs are quickest to start and offer frontier-model quality, so we often route to them only as a governed fallback for the hardest queries.
- Is my data actually private with a self-hosted LLM?
- Yes. Prompts, documents and responses stay on your infrastructure; nothing is sent to an outside provider for local models. Access is restricted by SSO and roles, and every request is logged for audit.
- What hardware or GPU do I need?
- It depends on model size and concurrency. Small quantised models run on a single modern GPU or even CPU via llama.cpp; larger models and many simultaneous users need more GPU VRAM, which vLLM serves efficiently. We size the hardware to your workload — and SnapSiteBuild's own node runs on AMD ROCm GPUs.
- Which models and engines do you use — Ollama, llama.cpp or vLLM?
- We run open models on Ollama for fast deployment, vLLM for high-throughput production serving and llama.cpp for efficient quantised inference on limited hardware — chosen per workload, sometimes combined behind one routing endpoint.
- How are token quotas and audit logs handled?
- The gateway enforces per-user and per-team token quotas to protect shared capacity and give finance showback, and records a full audit log of who used which model, when, with how many tokens and which retrieved sources — visible in the admin dashboard.
Ready to run private AI on your own infrastructure? Contact SnapSiteBuild for a local LLM setup plan covering hardware sizing, model selection, RAG, governance and rollout — or try the live endpoint first through our developer access.
Contact SnapSiteBuild: Customersupport@snapsitebuild.com