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High-performance GPU infrastructure purpose-built for enterprise AI training, inference and research — dedicated instances, reserved capacity and managed clusters with InfiniBand networking, tiered storage and transparent pricing.
NOVACORE Compute delivers the raw GPU power that modern AI workloads demand — from dedicated single-tenant servers through reserved capacity blocks to fully managed multi-node clusters. The first commercial offering is dedicated and reserved GPU infrastructure for enterprise AI training, inference and private deployments. Capacity is procured only after rigorous commercial and technical validation: target workload defined, utilisation modelled across multiple scenarios, unit economics confirmed, power and cooling verified. No GPUs are ordered on headline performance alone. This discipline protects both NOVACORE and its customers from the financial and operational consequences of speculative infrastructure investment.
Entire GPU servers with strong physical isolation for enterprise workloads requiring complete tenancy separation and predictable per-server billing.
Contracted monthly, quarterly or annual GPU capacity with discounted rates and guaranteed availability for teams with predictable baseline workloads.
Clusters built for distributed model training with InfiniBand fabric, parallel storage and fault-tolerant scheduling — from fine-tuning to full pre-training runs.
Low-latency, high-throughput model serving with autoscaling, cost controls, full observability and governance embedded at the serving layer.
High-performance object, file and block storage tiered for AI I/O patterns — from training checkpoint throughput to low-latency inference KV-cache access.
High-bandwidth InfiniBand fabric for east-west GPU-to-GPU traffic with logically segmented Ethernet networks for workloads, storage and management.
Kubernetes orchestration, GPU drivers, storage provisioning, network configuration and monitoring fully managed — so AI teams focus on models, not infrastructure.
Virtual and dedicated GPU-accelerated workstations for data scientists and ML engineers — preconfigured with frameworks, tools and secure access to compute and storage.
| Model | Isolation | Best for |
|---|---|---|
| Dedicated server | Full physical | Enterprises requiring predictability and compliance |
| Virtual GPU instance | Logical | Development, experimentation and flexible workloads |
| Managed AI cluster | Full physical or logical | Teams without dedicated platform engineering staff |
| Reserved capacity | Full physical or logical | Forecastable demand, procurement cycles and budget planning |
GPU hardware is purchased only when target workload, expected utilisation, customer commitments, power and cooling are confirmed. Unit economics are modelled at 30%, 50%, 70% and 85% utilisation before any purchase order is raised. No GPUs are bought on headline teraflops, marketing benchmarks or speculative demand projections. This discipline protects the business and ensures every GPU deployed has a validated reason to exist.
Secure AI and high-performance computing for enterprises, governments and research.