_ Training and Inferencing Generative AI Models
_ High-Performance Computing (HPC)
_ Drug Discovery
_ Algorithmic Trading
_ Genomics Analysis
_ Fraud Detection
We group our work around four key infrastructure domains. These are the components that make or break an AI factory - and the areas where our customers see the most variability in performance, cost, and scalability. Each deep dive explores common decisions, trade-offs, and real-world guidance based on active deployments.
From single GPU systems to SuperPOD-scale clusters, we help design and deliver the compute infrastructure that drives your AI factory. Whether you’re training multi-billion parameter models, fine-tuning domain-specific LLMs, or deploying inference at scale, we work with you to spec the right systems – from CPU/GPU balance to rack layout and energy draw.
We regularly deploy NVIDIA-certified systems across DGX, HGX, MGX, and Superchip architectures, and can advise on how they fit with your workload, physical environment, and long-term goals. Every deployment starts with real-world constraints: space, power, cooling, budget. We help model and optimise around those.
We regularly help customers with the very latest releases from NVIDIA, such as the Grace Hopper GH200 Superchip and the DGX Spark. From design to post-deployment, we help you understand how each building block fits into your AI factory.
Your network defines your AI factory. Every workload relies on fast, efficient data transfers between – or within – systems. Your choice of networking is a huge factor in maximising the value from NVIDIA’s world-leading compute.
Whether you need the throughput of NVLink and the scale of InfiniBand, or an Ethernet solution like Spectrum-X that slots into your existing layout, as an NVIDIA Elite Partner for Networking, we’ll help you design the right topology for your workloads.
From small test clusters up to 100+ GPU pods, we help you navigate vendor options, port layouts, rail-optimised designs, network automation, and power or cooling. The last thing you want is for your highly valuable compute and storage resources to be held back by inefficiencies in the network.
AI factories use storage differently at each stage of the pipeline. Ingest, preparation, training, inference, and augmentation all rely on data being available, consistent, and fast enough to keep GPUs active.
Many teams use separate storage systems for each stage: object stores for ingest, warehouses for prep, shared flash for training, and vector DBs for augmentation. Others use a single platform to handle the full lifecycle in one place.
We help you understand how data moves through your pipeline and design storage to support that behaviour: whether that means layering complementary systems or consolidating them into one workflow.
Software is what turns your cluster into a functioning AI factory. We help you deploy and tune infrastructure that supports NVIDIA’s core software ecosystem and the broader AI stack around it.
Many organisations standardise on NVIDIA AI Enterprise: a full-stack suite that includes Base Command, Run:ai, NIM microservices, Blueprints, and validated support for training and inference at scale. Mission Control is available separately for teams managing full-factory operations.
We help size and configure infrastructure to meet minimum software requirements: including interconnect, storage, and orchestration needs. That means less risk at rollout, smoother integration, and faster time to value from your existing hardware.
We help teams plan, deploy, and optimise the infrastructure behind their AI workloads – from the first GPU to full-scale production. Whether you’re comparing platforms, sizing a cluster, or designing for real-world constraints, we can help.
Need to test performance or validate your stack? Our AI Lab gives you remote access to the latest NVIDIA systems, including Grace Hopper and Spectrum-X, backed by our in-house engineering team.
