ETT + NVIDIA
ETT designs AI infrastructure on NVIDIA hardware, including dedicated H100 GPU capacity delivered through CUDO Compute, so clients get predictable performance for training and inference without owning a data centre.
About NVIDIA
NVIDIA is the world's leading accelerated-computing company. Its GPUs, networking and CUDA software stack are the de facto standard for training and serving modern AI models, from research labs to hyperscale data centres.
Enterprise AI workloads — model fine-tuning, retrieval pipelines, inference at scale — overwhelmingly run on NVIDIA hardware such as the H100 class of data-centre GPUs.
How NVIDIA fits into how we work
Every AI workload eventually meets a hard physical reality: it has to run on something. NVIDIA hardware is where modern AI actually happens, and that makes it foundational to the ETT story. When we talk about helping a business build AI native to how it works, NVIDIA's accelerated computing is the ground that ambition stands on.
ETT's value is not in reselling chips. It is in designing the right infrastructure around them, so a client gets the performance NVIDIA hardware is capable of without the cost, lead times and operational burden of building their own GPU estate. We bring that capacity to clients through dedicated, governed environments rather than leaving them to wrestle with hardware procurement and data-centre logistics.
How we turn the NVIDIA partnership into client outcomes
Technology alone doesn’t create value — how it’s applied does. This is the approach we take to make sure the NVIDIA relationship delivers something a client can measure and trust.
Right-size the compute
GPU capacity is expensive and easy to over- or under-provision. We size infrastructure to the actual workload, balancing training and inference needs so clients pay for performance they use rather than capacity that sits idle.
Dedicated, not shared guesswork
For sustained AI workloads we provision dedicated H100-class capacity through CUDO Compute, giving predictable performance and cost rather than the variability of contended public cloud GPU pools.
Built to scale with the client
We design infrastructure that can grow from a first proof of value to production scale without re-architecting, so early momentum is not lost when a workload proves itself.
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