Private & local LLMs · Australia

Your data. Your hardware. Your control.

Cloud frontier models (ChatGPT, Claude, Gemini, Copilot) win on peak capability and convenience — but every prompt leaves your control. Private LLMs keep every token on your machine (or private infrastructure). The trade-off is real: less peak reasoning, more setup, a hardware bill. This site helps Australians decide intelligently, set up correctly, and avoid expensive mistakes.

Honesty filter: Private LLMs give you real control and sovereignty, but they are not magic and they are not free.

v1.2.0 · Last verified: August 2026

Independence: Independent educational resource. Not affiliated with Meta, Alibaba, Mistral, Google, Microsoft, OpenAI, Anthropic, NVIDIA, Apple, Ollama, LM Studio, or any model, tool or hardware maker. Accuracy: content is educational and may be incomplete or outdated — verify official docs and live prices before you buy or deploy. See disclaimer and affiliate disclosure.

Master decision hierarchy

Map every recommendation through these four axes — the site’s core differentiator.

1. Privacy / sensitivity need

High (regulated data, legal/health/finance, client privilege, air-gapped) → private strongly preferred. Medium → hybrid often sensible. Low → cloud often wins on capability.

2. Budget & existing hardware

Start with what you own. Low extra budget → small models. Mid budget → stronger GPU or more unified memory for mid-size models. High budget → workstation-class options. Exact B-counts and prices move — treat as bands, not quotes.

3. Primary use-case

Casual chat/summaries; coding & agents (see vibecoding.au); RAG over private docs; fine-tuning / multi-user serving; fully offline / air-gapped.

4. Honest exit ramps

Need frontier reasoning daily with zero hardware budget, or a non-technical team needing polished multimodal → stay on ChatGPT/Claude Team (or hybrid) and revisit later.

Full walkthrough on Getting started · What is a private LLM?

Pick a path

Four common Australian starting points — not one-size-fits-all.

Local vs cloud at a glance

Data flow is the point — not hype.

Who this site is for

Practical operators — not AI theatre.

Built for

  • Australian SMBs and IT decision-makers wary of sending client or internal data to public cloud AI
  • Privacy-conscious individuals and developers who want local control
  • Regional and offline-first users who cannot rely on always-on APIs
  • Hobbyists choosing first hardware without getting upsold into a mining rig

Not built for

  • People seeking “set and forget frontier AI for free on a phone” hype
  • Anyone needing official vendor support (use the model/tool maker’s docs)
  • A substitute for legal, security, or compliance advice

Quick answers

What is a private LLM?

A large language model you run on hardware you control (laptop, desktop, Mac, mini-PC, or private server) so prompts and documents do not need to leave that environment. Contrast with ChatGPT/Claude/Gemini where inference runs in the vendor cloud.

Full explainer →

Is local as good as ChatGPT?

Usually not on peak reasoning, tools, and multimodal polish. Private wins on data control, offline use, and predictable cost after hardware. Cloud often wins when you need frontier quality every day and have low sensitivity data.

Private vs cloud comparison →

What hardware do I need in Australia?

Start with what you own (especially Apple Silicon or 32 GB RAM PCs). Upgrade storage and RAM first, then GPU if you need larger models. RTX 50-series is on AU shelves alongside 40-series; buy VRAM you can actually stock at a sane street price. Check live retailer listings — not overseas hype lists.

Hardware guide →

Is this legal or compliance advice?

No. Notes about the Privacy Act and APP 8 are general educational orientation only. Get qualified advice for regulated data.

Disclaimer →

Are model and hardware numbers guaranteed?

No. Family tables and hardware tiers are orientation aids. Model IDs, VRAM needs, licences, and AUD prices change. Verify official model cards and live retailer listings.

Read the full disclaimer →

Next step

Install a runner, pull a small model, run one prompt. Upgrade hardware only after the first win.