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Infrastructure

Local LLM
Infrastructure

On-premise large language model deployments using your own GPU hardware. Run inference locally with zero cloud dependency and zero per-query costs.

80B
Max Parameters
$0
Per Query Cost
Dual
GPU Platform Support
100%
Air-Gap Capable

How It Works

We deploy a complete AI stack on your hardware — from GPU drivers to a production-ready API. Open-source models like Llama 3, Qwen, and Mistral run locally through an inference engine, exposed via an OpenAI-compatible API that your applications connect to with zero code changes.

Local LLM infrastructure stack showing GPU hardware, inference engine, AI models, OpenAI-compatible API, and applications
1

Hardware Setup

We specify and configure GPU hardware (NVIDIA RTX or Apple Silicon) optimised for AI inference. Drivers, CUDA, and runtime installed and tuned.

2

Model Deployment

Open-source AI models are deployed via Ollama, vLLM, or MLX. Models are selected and quantised for your hardware and use case.

3

API & Integration

An OpenAI-compatible REST API is exposed on your network. Existing tools and applications connect with minimal configuration changes.

Cloud AI vs Local LLM

Cloud AI (ChatGPT, etc.)

  • ✗ $0.01 - $0.10 per query
  • ✗ Data sent to external servers
  • ✗ Internet required for every query
  • ✗ Vendor can change pricing or API
  • ✗ Usage-based billing scales with growth

Local LLM (Your Hardware)

  • ✓ $0 per query — unlimited usage
  • ✓ Data never leaves your premises
  • ✓ Works offline / air-gapped
  • ✓ You own and control everything
  • ✓ Fixed cost regardless of usage

Technical Details

Ollama vLLM MLX NVIDIA CUDA Llama 3 Qwen Mistral CodeLlama
Supported Models & Performance

Llama 3 (8B, 70B): Meta's open-source models. Excellent general capability. 70B runs on 48GB+ VRAM or 64GB+ unified memory.

Qwen (7B, 14B, 72B): Strong multilingual and reasoning capability. 72B competitive with GPT-3.5 on many benchmarks.

Mistral (7B) / Mixtral (8x7B): Efficient inference with mixture-of-experts architecture. Good quality at lower resource cost.

CodeLlama (7B, 34B): Specialised for code generation, review, and explanation. Ideal for technical teams.

Quantisation: Models are quantised (4-bit, 5-bit, 8-bit) to fit your hardware while preserving quality.

Hardware Recommendations

Entry Level: NVIDIA RTX 3060 (12GB) or M1 Mac (16GB). Runs 7B models comfortably for single-user applications.

Recommended: NVIDIA RTX 3090 (24GB) or M1/M2 Max (32-64GB). Runs 13-34B models. Good for small team usage.

Professional: NVIDIA RTX 4090 (24GB) or M2 Max (96GB). Runs 70B+ models. Multi-user concurrent access.

Enterprise: Dual RTX 4090 or Mac Studio with M2 Ultra (192GB). Maximum capability for large organisations.

Who This Is For

Regulated Industries

Legal, medical, financial — where data sovereignty is non-negotiable. Air-gapped deployment available.

High-Volume Users

Businesses making thousands of AI queries daily. Fixed hardware cost vs escalating cloud bills.

Development Teams

Code assistance, documentation generation, testing — all running locally with zero per-query cost.

Security-Conscious

Any organisation that cannot send proprietary data to external AI services. Your data, your hardware, your control.

Frequently Asked Questions

What is a local LLM and why would I want one?
A local LLM (Large Language Model) is an AI system like ChatGPT, but running entirely on your own hardware. This means your data never leaves your building, you pay zero per-query fees, you have no dependency on internet connectivity or cloud providers, and you own the entire system outright. For businesses handling sensitive data, this is a critical advantage.
How does a local LLM compare to ChatGPT or Claude?
Cloud AI services like ChatGPT charge per query, require internet connectivity, and process your data on external servers. A local LLM runs the same type of AI models on your own GPU hardware at zero marginal cost per query. Trade-off: the largest cloud models (GPT-4, Claude) are more capable than what runs locally, but local models (Llama 3 70B, Qwen 72B) handle most business tasks excellently.
What hardware do I need?
For most business applications, an NVIDIA RTX 3090 (24GB VRAM) or Apple Silicon Mac with 32GB+ unified memory. Larger models (70B+ parameters) benefit from RTX 4090 (24GB) or Apple Silicon with 64GB+. We specify, source, and configure all hardware as part of the project.
Can existing applications connect to the local LLM?
Yes. The local deployment provides an OpenAI-compatible API. Any application or tool that currently uses OpenAI/ChatGPT can be pointed at your local LLM with minimal code changes. This includes chat interfaces, document processing tools, code assistants, and custom applications.
What about air-gapped deployments?
We support fully air-gapped deployments where the AI system has zero internet connectivity. Models and software are loaded offline. This is suitable for classified environments, high-security facilities, and organisations with strict data sovereignty requirements.

Own Your AI Infrastructure

Stop renting AI from cloud providers. Deploy LLMs on your hardware with zero per-query costs and complete data sovereignty.

Book a Free Consultation Call 0400 819 487