On-Premises LLM Deployment in Australia
Run your custom AI entirely on your own servers. Zero internet dependency, sub-50ms latency, complete control over every component. The ultimate in data sovereignty and performance for Australian enterprises.
Why On-Premises Matters
For organisations where data must never leave the building — whether for regulatory, security, or strategic reasons — on-premises deployment is the only option that provides absolute assurance.
Absolute Data Control
With on-premises deployment, your data never leaves your physical premises. There is no cloud provider, no network egress, and no third-party access of any kind. For organisations handling classified government data, privileged legal information, patient health records, or proprietary trade secrets, this level of control is non-negotiable. Even sovereign cloud solutions involve a managed service provider — on-premises eliminates this entirely.
Superior Performance
On-premises deployment eliminates the network latency inherent in cloud-based AI services. Where a cloud API call adds 100 to 300ms of network round-trip time, on-premises inference happens in under 50ms on local hardware. For applications requiring real-time AI responses — interactive document analysis, live customer service assistance, or time-sensitive compliance checks — this performance difference is transformative.
Air-Gap Capable
Certain environments require complete network isolation: defence contractors, intelligence agencies, critical infrastructure operators, and organisations handling the most sensitive commercial data. On-premises LLM deployment supports fully air-gapped operation where the AI system has no network connectivity whatsoever. Updates and model improvements are delivered via offline media transfer.
Architecture Overview
From site assessment to go-live, the deployment process is structured to minimise disruption and ensure reliable operation from day one.
Site Assessment
We assess your data centre or server room: power capacity, cooling, network connectivity, physical security, and existing infrastructure. A detailed hardware specification and network architecture is produced.
Hardware Procurement & Setup
Server hardware is procured, configured, and stress-tested. We handle GPU driver installation, OS hardening, network configuration, and security baseline implementation. All done on-site or pre-staged at our facility.
Software Deployment & Training
The LLM inference stack, RAG pipeline, API layer, and monitoring tools are deployed. Your model is fine-tuned on your data and validated. Integration with your business systems is configured and tested.
Go-Live & Managed Operations
The system goes live with your team trained on day-to-day operations. Ongoing managed services include monitoring, model updates, security patching, and performance optimisation.
Deployment Options
Choose the deployment model that fits your existing infrastructure and operational preferences.
Bare Metal
Maximum performance with direct hardware access. The LLM runs directly on your server hardware without virtualisation overhead, delivering the best possible inference latency and throughput. Ideal for organisations with dedicated AI hardware that want to extract every bit of performance.
- Zero virtualisation overhead for maximum GPU utilisation
- Direct hardware access for optimal memory bandwidth
- Best option for high-throughput production workloads
- Supports NVIDIA multi-instance GPU (MIG) partitioning
VMware / Hypervisor
Deploy within your existing virtualisation infrastructure. The LLM runs inside a VM with GPU passthrough, integrating with your standard VM management workflows, backup procedures, and monitoring tools. Compatible with VMware vSphere, Proxmox, and Hyper-V.
- GPU passthrough for near-native performance
- Integrates with existing VM lifecycle management
- Snapshot and backup compatibility
- Resource isolation from other workloads
Kubernetes
Cloud-native deployment using container orchestration. The LLM runs in Kubernetes pods with GPU scheduling, auto-scaling, and health monitoring. Ideal for organisations with existing Kubernetes infrastructure who want elastic scaling and declarative configuration.
- Auto-scaling based on inference demand
- Rolling updates for zero-downtime model upgrades
- GPU resource scheduling and multi-model support
- Helm charts for repeatable, version-controlled deployment
Hardware & Software Stack
A reference architecture for on-premises LLM deployment. Exact specifications are tailored during the site assessment based on your workload requirements.
Hardware Requirements
- GPU: 1-4x NVIDIA A100/H100 (80GB VRAM) or equivalent
- RAM: 128-512GB DDR5 ECC (depending on model size)
- Storage: 2-8TB NVMe SSD (model weights + vector database)
- Network: Redundant 10GbE minimum (25GbE recommended)
- Power: 2-6kW per server (UPS and generator backup required)
Software Stack
- OS: Ubuntu Server 22.04 LTS (hardened configuration)
- Inference: vLLM or TensorRT-LLM for optimised serving
- Vector DB: Qdrant or Milvus for RAG retrieval
- API: FastAPI gateway with rate limiting and auth
- Monitoring: Prometheus + Grafana for performance metrics
Cost Comparison: On-Premises vs Cloud vs SaaS
Understanding the true cost of each deployment model helps make an informed infrastructure decision based on your scale and timeline.
On-Premises
Sovereign Cloud
SaaS (per-user)
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Common questions about on-premises LLM deployment for Australian enterprises.
Ready for On-Premises AI?
Book a site assessment to understand exactly what your on-premises LLM deployment will look like: hardware requirements, network architecture, timeline, and total cost of ownership.