On-Premises LLM Deployment in Australia

The hardware requirements depend on your model size and throughput needs. For a standard enterprise deployment serving 50 to 200 concurrent users, we typically recommend a server with 2x NVIDIA A100 or H100 GPUs (80GB VRAM each), 256GB system RAM, 2TB NVMe storage, and redundant 10GbE networking. Smaller deployments can run on a single A100 or even consumer-grade GPUs like the NVIDIA RTX 4090 for teams under 20. We provide a detailed hardware specification as part of our assessment.

Yes. Once deployed, the on-premises LLM operates entirely within your local network with zero internet dependency. All inference, data processing, and model serving happen on your hardware. The only time internet connectivity is required is for initial software deployment and periodic model updates, both of which can alternatively be done via offline media transfer for air-gapped environments.

On-premises deployment typically delivers better inference latency than cloud-hosted solutions because there is no network round-trip to an external data centre. Our standard deployments achieve sub-50ms time-to-first-token latency for typical enterprise queries. Throughput depends on hardware: a dual A100 setup handles 200 to 400 concurrent requests with consistent performance. For comparison, cloud API calls typically add 100 to 300ms of network latency alone.

On-premises deployment has higher upfront capital expenditure but lower ongoing operational costs. A typical enterprise setup costs $80,000 to $150,000 in hardware plus $3,000 to $8,000 per month in managed services. Over three years, this is typically 30 to 50 percent less expensive than cloud-hosted equivalent capacity, and 60 to 70 percent less than SaaS per-user pricing for organisations with 200 or more users. The financial case strengthens with scale and time.

We offer three management models. Fully managed: our team handles all hardware monitoring, software updates, model retraining, and incident response via secure remote access. Co-managed: your IT team handles hardware and OS while we manage the AI stack. Self-managed: we provide documentation and training for your team to manage everything independently with optional support tickets. Most enterprise clients choose the fully managed or co-managed model.

Absolutely. This is a common pattern. Many organisations start with our sovereign cloud deployment to validate the AI use case and quantify ROI, then migrate to on-premises once the business case is proven. The migration path is straightforward because the same model, configuration, and integrations transfer directly. We handle the migration with minimal downtime, typically completing it over a weekend.