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Clusters engineered for distributed training at scale — with high-bandwidth east-west fabric, coordinated parallel storage and a disciplined approach to unit economics that ensures every GPU deployed has a validated workload and a clear utilisation target.
Training foundation models and large language models requires infrastructure that goes far beyond stacking GPUs in racks. Every element — from the network fabric that carries gradient synchronisation across hundreds of nodes, to the parallel filesystem that sustains checkpoint write throughput, to the job scheduler that handles node failures gracefully — must be engineered as a coherent system. NOVACORE approaches training infrastructure with the same discipline: define the target workload first, model utilisation across multiple scenarios, calculate unit economics per configuration, confirm power and cooling, and only then order capacity in phases. This methodical approach protects both NOVACORE and its customers from the consequences of buying GPUs on headline performance alone.
We never order GPUs on headline performance alone. Every training cluster deployment follows a rigorous sizing methodology that models cost, utilisation and return across multiple scenarios before a single purchase order is raised.
Scale from single-node 8-GPU setups to multi-rack clusters spanning hundreds of H100s. Our fabric and scheduler are engineered for tightly-coupled distributed training with data parallelism, tensor parallelism and pipeline parallelism strategies optimised for each workload profile.
Pre-training, continued pre-training and full-weight fine-tuning of large language models from 7B to 70B+ parameters. Our infrastructure is sized for the memory bandwidth, network throughput and checkpoint I/O that frontier model training demands — not repurposed from general-purpose cloud.
Parameter-efficient approaches including LoRA, QLoRA and adapter-based methods for teams that need to adapt foundation models to domain-specific tasks without the cost of full-weight training. Our infrastructure supports rapid iteration cycles with fast model loading and evaluation pipelines.
Never buy GPUs on headline performance alone. NOVACORE builds a unit-economics model for each configuration covering acquisition cost, power draw, cooling overhead, financing terms, support staffing and expected utilisation before ordering a single GPU. This discipline protects the business and ensures customers only pay for capacity that has a validated demand profile and a sustainable operating model behind it.
Secure AI and high-performance computing for enterprises, governments and research.