Over the past year, large language models (LLMs) like OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini have demonstrated just how transformational AI can be across a wide range of industries — from customer service and legal analysis to data entry, planning, and software engineering.
While these cloud-based solutions offer top-tier accuracy and scale, they aren’t the only option. Thanks to open-source innovation and rapid hardware advances, many LLMs can now run locally — on your own servers, workstations, or even high-end desktops.
But should you?
Let’s take a clear-eyed look at the pros, cons, and context — especially for Australian businesses evaluating both cost and compliance.
Why Consider Local LLMs?
Running language models on your own infrastructure has some compelling advantages — but also important trade-offs.
✅ Advantages
- Privacy & Data Control
Keeping data in-house avoids third-party exposure and minimizes the risk of accidental leaks or platform-side breaches. This is especially relevant for sensitive or regulated data (legal, financial, medical, etc.).
- Legal & Sovereignty Compliance
Australian data sovereignty requirements — particularly in government and finance — can be more easily met by avoiding offshore or opaque infrastructure.
- Predictable Cost at Scale
Cloud AI costs are usage-based and can balloon with heavy workloads. By contrast, local LLMs have upfront hardware costs, but often lower ongoing marginal cost per use.
- No Vendor Lock-in or API Rate Limits
You control uptime, performance, and access — and are not dependent on cloud APIs or commercial pricing changes.
⚠️ Trade-offs
- Initial Setup and Outlay
A capable local LLM setup typically requires $3,000–$7,000 AUD upfront for sufficient GPU and RAM, depending on your use case. (Higher if multiple users will run models concurrently.)
- Ongoing Power and Maintenance Costs
A local AI rig can consume 150–300W under load, adding $30–$60 AUD/month in electricity. Maintenance, updates, and support fall to you — not a vendor.
- Model Limitations
The largest and most capable models (like GPT-4 Turbo or Claude 3 Opus) still vastly outperform local models in nuanced reasoning, multi-step tasks, and creative writing. Locally, you’ll typically get 70–90% of that capability — often “good enough,” but not cutting-edge.
- Security ≠ Simplicity
While local LLMs offer data control, true security still depends on careful setup: encrypted storage, patched systems, user isolation, and audit trails. For many, using a secure Azure-hosted API may be safer and more practical.
Cloud vs. Local — It’s Not Either/Or
For most teams, the ideal solution isn’t only local or only cloud. Instead, it’s about picking the right tool for the task:
| Use Case | Best Option |
| Top-tier accuracy, reliability | Cloud APIs (e.g., GPT-4, Claude) |
| In-house data processing, prototypes | Local LLMs (e.g., DeepSeek, Mistral) |
| Multi-user interactive chat | Cloud or hybrid model |
| Edge/offline use | Local only |
| Strict data sovereignty | Local or Azure GovCloud AU |
Azure and other providers are closing the gap. With tools like Microsoft’s private endpoint access, dedicated region hosting, and encrypted vaults, it’s increasingly possible to achieve similar security and compliance — without managing everything yourself.
Choosing which depends on your exact needs and requirements, the aim of this series of articles is to explore the Local LLM option and to see whether it can be used to replace or augment your commercial cloud offerings.
Looking Ahead 🔍
In this series, we’ll walk through:
- How to evaluate your current hardware for AI readiness
- What open-source models are worth testing in 2025
- How to deploy local models safely and cost-effectively
- Real world benchmarks
- When it makes sense to switch back to cloud — and how to integrate both
Local LLMs aren’t a silver bullet — but they can be potentially a powerful addition to your AI toolbox, depending on your requirements.