LOCAL LLM DEPLOYMENT, BUILT FOR CONTROL
Local LLM deployment means running an open-source language model — such as Llama 3, Mistral, Gemma 4, or Qwen — on hardware inside your office or data centre. No queries, documents, or AI responses pass through OpenAI, Google, or Microsoft. We handle model selection, hardware sizing, RAG configuration, and access controls for Melbourne organisations that need AI capabilities without third-party data exposure.
Your staff want AI tools. Your compliance team says no to ChatGPT. This is the middle ground: a system that runs inside your network, searches your documents, and gives answers with source citations — without a single byte leaving the building.
LOCAL DEPLOYMENT OPTIONS
Run on an existing server, a new GPU workstation, or dedicated rack-mounted hardware. We work with what you have or spec what you need — the model runs where your IT team can see it.
PRIVATE INTERNAL OPERATIONS
Summarise matter files, search internal policies, answer compliance questions — all processed locally. No API calls to external services, no data retention by third parties, no cloud dependency.
SECURE DOCUMENT ACCESS
Role-based access controls ensure each team only sees their own documents. Ethical walls, department boundaries, and matter-level restrictions are enforced at the system level — not by policy alone.
LOCAL LLM VS CLOUD AI
| Factor | Self-Hosted (What We Deploy) | Cloud AI (ChatGPT, Copilot, Gemini) |
|---|---|---|
| Data location | On premise / private infrastructure | External servers, often overseas |
| Privacy Act 1988 compliance | Full control over data handling | Requires detailed assessment of APP 8 |
| Auditability | Full logging and access control | Limited to provider's audit features |
| Customisation | Fine-tuned for your documents and workflows | General-purpose, limited customisation |
| Cost model | One-time setup + ongoing support fee | Per-user monthly subscription, scales with usage |
| Model flexibility | Swap models anytime — Llama 3, Mistral, Gemma 4, or the next open-source release | Locked to the provider's model and update schedule |
USE CASES
"Most people ask which model to use first. That's actually the easy part — the open-source options are strong and improving fast. The harder question is how you structure the retrieval pipeline, what documents to index, and how to configure access controls so the right people see the right information. That's where the deployment either works or doesn't."
FREQUENTLY ASKED QUESTIONS
What is local LLM deployment?
Local LLM deployment means running an open-source language model — such as Llama 3, Mistral, Gemma 4, or Qwen — on a server inside your office or data centre. No queries, documents, or AI-generated responses pass through OpenAI, Google, or any other external platform. Everything stays on hardware you own and control. This is the approach we specialise in for Melbourne organisations subject to the Privacy Act 1988, APRA prudential standards, or professional confidentiality obligations.
What hardware is required for local LLM deployment?
It depends on the model size and how many people will use the system. A single GPU workstation with an NVIDIA RTX 4090 can run a 13-billion parameter model comfortably for a team of 10-20 users. Larger deployments — 50+ users or bigger models — typically use rack-mounted servers with A100 or H100 GPUs. We assess your specific workload during the initial consultation and recommend hardware that balances performance, cost, and your existing infrastructure. Many firms already have suitable hardware sitting underused.
How long does a local LLM deployment take?
Most deployments take 4 to 8 weeks from first conversation to production readiness. The timeline depends on your document volume, infrastructure complexity, and how many access control rules need configuring. A straightforward deployment for a 30-person firm with a single document set can be done in under four weeks. More complex setups — multiple practice groups, ethical walls, integration with existing systems — sit closer to eight. We follow a five-step process: Assess, Design, Build, Validate, Support.
Can local LLMs match the quality of cloud AI services?
For the tasks most firms need — document summarisation, question answering, policy search, and analysis — modern open-source models like Llama 3 and Mistral perform comparably to ChatGPT. They won't write poetry as well, but that's not what you're deploying them for. We select and fine-tune models for your specific use case, which often means better results than a general-purpose cloud tool because the system is trained on and retrieves from your actual documents. The quality gap that existed two years ago has largely closed for enterprise workloads.
Want to See How This Works for Your Firm?
We'll walk you through a deployment that fits your setup — your documents, your infrastructure, your compliance requirements. No sales pitch.
Request a Consultation →Or email us directly at hello@airgapllm.com.au