NVIDIA
We size and build GPU estates on NVIDIA for training and inference, then prove the stack under a real workload.
What We Do on NVIDIA
An NVIDIA engagement starts with the workload. We size the graphics processor (GPU) estate from your models, data and response times. Then we design compute, networking, storage, power and cooling as one system, because a fast card on a slow network wastes its speed. The work ends with a measured test, not a delivery note.
The named engagements are a capacity model, a cluster design, a model-serving service and a utilisation review. CUDA, the programming toolkit models use, is part of the stack we test. NVIDIA NIM packages inference services that can run inside your environment. We exercise the whole path before handover.
You provide the models you intend to run, the data they use and the latency each service needs. You decide where the estate sits and who may use it. You receive a capacity model, a tested deployment and a monitoring plan.
The Services
The catalogue services we run on NVIDIA hardware, each with its part here.
- Generative AI ApplicationsGenerative models serve on NVIDIA GPUs with retrieval, evaluation and guardrails.
- AI Agents and AutomationAgents call models on your NVIDIA estate with permissions and an audit trail.
- Private AIModels run on NVIDIA hardware in your environment, under your rules.
- Speech and Vision AISpeech and vision models run on NVIDIA GPUs near the data they read.
- Machine Learning and Predictive AnalyticsPredictive models train and serve on NVIDIA GPUs, with drift monitoring.
- Hybrid Cloud and Data CentreGPU workloads run in your data centre or a private cloud, with the network designed for them.
- AI InfrastructureWe size and run the GPU estate: compute, network, storage, power and cooling.
- Managed Cloud and Site ReliabilityWe monitor the GPU estate, plan capacity and respond to incidents.
- OT and IoT SecurityInspection models read industrial devices through a separated network.
- AI SecurityWe test model access, prompt handling and data paths on the NVIDIA estate.
Where These Projects Go Wrong
GPU projects fail on the same three points. Each is measurable before the hardware is ordered.
Capacity Bought by the Card
Teams buy GPUs without matching them to the model or the load. We size from measured workloads, then plan headroom for peaks.
Utilisation Is Invisible
Cards sit idle while one team queues and another holds capacity. We add scheduling, quotas and a use report with named owners.
The Stack Is Untested
A cluster is handed over before the model runs at the promised speed. We test with a real workload and record the result before handover.
How a Project Starts
The opening weeks turn models and response times into a capacity number.
Model the Workload
We review the models, data and response times you need. You receive a capacity model and a constraint list. You decide which workload proves the design.
Design the Estate
We design compute, network, storage, power and cooling as one system. The load test plan is part of the design. You approve it before purchase.
Run the Load Test
We deploy the stack and run the chosen workload under load. You receive the measured throughput and the monitoring plan. You decide whether to scale.
Questions
The questions buyers bring to a first NVIDIA discussion.
How do you decide how many GPUs we need?
We model the models, data and response times you need. A test workload shows the real use before you commit to a larger estate.
Can the estate run in our own data centre?
Yes. We design for your data centre or a private cloud. Power, cooling and network are part of the design.
How do we stop teams competing for capacity?
We add scheduling, quotas and a use report with named owners. Each team sees the capacity it holds.
Can we run several models on one estate?
Yes. We plan for current models and expected ones. Serving and scheduling keep them apart.
How do you protect the models and data?
Access runs through your identity provider, data stays in your environment, and every model call is logged. We test the controls before handover.
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