Trusted by Leading Organisations
Fine-tune open-source LLMs on air-gapped Kubernetes. No tokens leave your premises. No third-party API dependencies.
Trusted by Leading Organisations
The Problem
Your IP, your customer data, your competitive advantage — in someone else’s logs. For regulated industries, classified environments, and any organisation that takes sovereignty seriously, public AI APIs are not a risk to be mitigated. They are architecturally disqualified.
The alternative is not weaker AI. It is AI that runs entirely inside your perimeter — fine-tuned on your data, serving your applications, with zero external dependency.
Read: ChatGPT Is Not an Option →100% Air-Gapped
All model weights, training data, and inference traffic stay within your cluster boundary.
No API Costs
Zero per-call charges. Unlimited inference on your own hardware at the cost of electricity.
Full Fine-Tune Control
Train on your proprietary data. Own the resulting model weights. No licences, no provider dependency.
What We Build
Three components. One fully automated, air-gapped pipeline from training data to production inference.
QLoRA fine-tuning with Kubeflow on dedicated GPU nodes. Mistral 7B, Qwen-VL, or your model of choice. Your proprietary data never leaves the cluster. Each training run is versioned and reproducible.
vLLM serving with autoscaling. Multiple concurrent streams. RTX A6000 or equivalent — at a fraction of cloud GPU pricing. Microsecond latency vs. 800ms round-trip to external APIs.
MLflow experiment tracking, automated retraining triggers, model versioning, rollback. The complete MLOps loop — on-premises. No external orchestration services required.
Supported Models
Any HuggingFace-compatible model. We have production experience with:
Use Cases
Process contracts, compliance reports, and regulatory filings using a fine-tuned model that understands your domain vocabulary and classification requirements.
pgvector + embedding model gives you semantic search over your internal documentation, codebases, and knowledge bases — all inside your perimeter.
Trigger inference jobs on incoming data streams. Extract structured output from unstructured documents. Chain models together in air-gapped Kubernetes jobs.
Deploy a Retrieval-Augmented Generation (RAG) system that answers questions over your internal data. Your engineers and analysts get GPT-quality tooling without the compliance exposure.
Qwen-VL handles combined image and text tasks — document scanning with OCR context, visual inspection automation, diagram analysis — entirely on-premises.
Whisper large-v3 running on your cluster transcribes audio without any data leaving your network. Meeting recordings, call centre audio, classified briefings — all processed internally.
The Hardware
Production-grade AI compute at NEXTDC Sydney — Australian-owned, outside US CLOUD Act jurisdiction.
GPU per card
RTX A6000 — 48GB VRAM
Cards per server
2 cards (96GB total)
Parallel inference streams
6 streams across 3 servers
Colocation
NEXTDC Sydney (S1/S2)
Orchestration
Kubernetes GPU Operator
Observability
node-exporter + Grafana dashboards
Connected Service
Private AI on top of infrastructure we designed and built means we know every layer — from the GPU driver to the Kubernetes scheduler to the network path the inference traffic takes. When something needs tuning, we don’t start from documentation. We start from the cluster.
Learn about our infrastructure service →Tell us your use case. We’ll show you the exact stack, the hardware cost, and what it replaces.
