Private enterprise AI
Assistants, retrieval and agents over proprietary data that has to stay inside the organisation.
Safina Neocloud GPU as a Service
Safina runs high-performance GPU capacity as a managed cloud, hosted in-country, so your data, models and keys stay under your own jurisdiction. Start with what you need, and grow one block at a time.
01 Why it matters
Where a model is trained and served decides whose rules apply to the data inside it. Safina hosts GPU compute in-country, with residency, access and key management kept under local control from the first day.
02 What Safina offers
Accelerators are where it starts. Safina wraps them in the layers that make capacity usable, safe and billable, and runs the whole stack for you.
Data-centre-class GPU servers, a high-bandwidth compute fabric, shared parallel storage, backup and recovery.
Enterprise Kubernetes, GPU scheduling, drivers and model-serving environments, ready for training and inference pipelines.
Authentication, tenant separation, quotas, metering, reporting and billing, with branded access for solution providers.
Encryption, logging, observability and service monitoring, with an operations team responsible for the whole stack.
03 How it works
Tell us what you need to run: model sizes, data sensitivity, timelines. We size capacity around the workload, not around a catalogue.
You get your own isolated environment: a separate tenancy, your own access controls, and keys held in-country.
Train, fine-tune and serve on managed GPU infrastructure, with monitoring and operational support built in.
Add capacity as demand grows. Usage is metered and reported, so you see exactly what you consume.
04 Built to scale
Safina grows in standard blocks. The first block is measured under real workloads: power, cooling, network and performance. Every block after it is designed from those measurements and validated before customer workloads move onto it.
05 What runs on Safina
Assistants, retrieval and agents over proprietary data that has to stay inside the organisation.
Serve models close to the people using them, over in-country data paths.
Adapt open and proprietary models on dedicated GPUs, without sending training data offshore.
Simulation and analysis for research teams that need accelerated compute on local terms.
Climate, soil and yield models that turn local data into local decisions.
Process satellite and remote-sensing imagery where it is collected and governed.
Demand, weather and risk forecasting on infrastructure you can audit.
Sovereign AI services for agencies that need residency and accountability by default.
Illustrative workloads
06 Why Safina
Compute, data, access and key management stay within the country they serve.
Every customer runs in a separate environment, with tenant separation and audit logging from the first day.
Performance is benchmarked on the platform itself and reported openly.
Capacity expands in validated, repeatable blocks, so growth never puts running workloads at risk.
07 Request capacity
Share a few details about the workload and the Safina team will follow up to scope it with you.
Or write to mbussacarini@csecns.au