Dedicated infrastructure for AI workloads that need more control
ETT helps organisations access and design GPU-powered infrastructure for AI model training, inference, MLOps and production-grade AI workloads. We support teams that need dedicated capacity, secure environments and infrastructure designed around the demands of AI-native systems.
Why this matters
AI workloads place different demands on infrastructure.
Teams building GenAI applications, LLM workflows, model training pipelines or inference services often need access to high-performance compute, predictable capacity, secure networking and better control over cost, data and deployment environments.
Shared cloud GPU instances can be useful, but they can also introduce challenges. Provisioning may be slow, performance can be inconsistent, costs can be difficult to forecast, and some workloads need stronger control over residency, compliance or dedicated capacity.
ETT helps organisations design AI infrastructure that fits the workload, not the other way around. From dedicated GPU hosting to MLOps environments and secure integration with AI tooling, we help teams create the technical foundation needed to build, run and scale AI with more confidence.
Why AI workloads need specialised infrastructure
AI infrastructure is not just about where something is hosted. The environment needs to support how models are trained, deployed, monitored and improved. That can involve GPU capacity, storage, networking, security controls, data access, orchestration, monitoring and integration with the wider AI tooling ecosystem.
For some organisations, shared cloud services are enough. For others, the workload demands more control. Dedicated AI infrastructure can help when teams need predictable compute, clearer data residency, reduced noisy-neighbour risk, stronger security design or closer alignment between infrastructure and the AI product roadmap.
The right infrastructure decision depends on the model, the data, the risk profile, the workload and the way the organisation plans to operate AI over time.
Shared capacity, unclear costs, slow provisioning, generic configuration, limited control.
Dedicated GPU capacity, clearer environment design, secure networking, AI-specific architecture, workload-aligned.
How we help
Hosted GPU infrastructure
We support secure, high-performance environments for AI workloads including model training, inference and LLM experimentation. Where approved and appropriate, this may include access to GPU capacity such as NVIDIA H100, A100 or L40s through ETT's infrastructure and partner ecosystem.
AI-native infrastructure consulting
We help organisations understand what infrastructure their AI workloads need, from compute requirements and data flows to security, networking and deployment architecture.
Networking and security design
We support the design of secure connectivity, access controls and networking layers across cloud, hybrid or dedicated environments.
MLOps and ModelOps platforms
We help teams create the operational backbone for production AI, including model deployment, monitoring, governance, pipeline management and lifecycle control.
AI tooling integration
We support integration with the wider AI ecosystem, including LLM workflows, RAG pipelines, vector databases, inference endpoints and existing enterprise platforms.
Data and workload management
We help organisations think through how data, compute and workloads should be managed so AI systems can run efficiently, securely and in line with business requirements.
When dedicated AI infrastructure makes sense
Building in-house AI applications
For CTOs and AI teams developing internal LLM applications, AI tools or model-driven products.
Technology that fits the workload
ETT works with specialist technology and infrastructure partners to help organisations access the right AI environment for the right use case.
Where approved, this may include dedicated GPU capacity, hosted AI environments, secure networking, MLOps tooling and infrastructure options through partners such as CUDO Compute.
The focus is always on the workload: what the model needs, how the data moves, what security controls are required, and how the environment will support live AI operation.
How we design infrastructure around AI workloads
Diagnose
We assess the AI workload, model goals, data requirements, performance needs, security requirements and current infrastructure constraints.
Design
We define the infrastructure architecture, GPU requirements, networking, storage, MLOps approach, integration points and governance considerations.
Deploy
We support the implementation of the infrastructure environment, connecting compute, data, tooling, security and operational controls.
Operate
We help review performance, capacity, cost, workload changes and operational requirements so the infrastructure continues to support AI use over time.
What sets this apart
ETT approaches infrastructure as part of the wider AI operating model.
The question is not only where the workload will run. It is what the AI system needs to do, what data it will use, how it will be secured, how it will be monitored and how it connects to the wider business environment.
That means infrastructure needs to be designed alongside strategy, data, security and delivery. ETT helps organisations make those decisions in context, so the technical foundation supports the AI capability being built.
Built around AI workloads
We design around model training, inference, MLOps and AI application requirements.
Connected to data and governance
Infrastructure decisions are shaped by data access, residency, security and compliance considerations.
Designed for production use
We help create environments that support AI systems beyond experimentation.
Supported by a partner ecosystem
Where appropriate, ETT can support access to specialist infrastructure capabilities through approved technology and infrastructure partners.
Often delivered alongside
Common questions
What is AI Infrastructure as a Service?
It provides the compute, hosting, networking and operational environment needed to build, train, deploy or run AI workloads.
What is dedicated GPU hosting?
Dedicated GPU hosting gives organisations access to GPU capacity for AI workloads such as model training, fine-tuning, inference or LLM experimentation, without relying solely on shared cloud instances.
When does a business need dedicated AI infrastructure?
When workloads require predictable compute, stronger control, specific security requirements, data residency, MLOps capability or reduced reliance on generic shared cloud environments.
What is MLOps?
The set of practices and tools used to deploy, monitor, manage and improve machine learning models in production environments.
Can AI infrastructure support RAG and vector databases?
Yes. It can support RAG pipelines, vector databases, inference endpoints, model hosting and the wider tooling needed for AI applications.
Does your AI workload have the infrastructure it needs?
Book an Executive AI Acceleration Session to explore the compute, data, security and MLOps foundations needed to build, train or run AI systems with more control.