AI Infrastructure, Wherever It Needs to Run
Layots Technologies builds AI infrastructure on-premise, in colocation, on private cloud or on a hyperscaler — with GPU, CPU or TPU compute — and runs it after go-live. From a first GPU server to a multi-rack training cluster or a private LLM, we size it, deploy it and operate it.
AI Infrastructure Inquiry
Tell us the workload; we will recommend where to run it and on what.
Choose where it runs
Four deployment models, one team. The right answer depends on utilisation, data residency, budget and how quickly you need capacity — and many estates combine two or more.
On-premise
Best for: Sustained training, strict data residency, existing data centre capacity.
- GPU and CPU servers in your own facility
- Power, cooling and floor-loading assessment first
- Full control of data, models and hardware
Colocation
Best for: Owning the hardware without building a data centre.
- Tier-3 and tier-4 facilities including Yotta, STT and Equinix
- Managed rack, power, cross-connect and remote hands
- High-density racks for GPU power and cooling
Private cloud
Best for: Cloud-like GPU access with data kept in a private environment.
- GPU as a Service on dedicated infrastructure
- Project and tenant isolation
- Kubernetes with GPU-aware scheduling
Hyperscaler
Best for: Bursty, experimental or short-lived workloads and fast starts.
- GPU capacity on Microsoft Azure, AWS, Google Cloud and Oracle Cloud
- TPUs on Google Cloud
- Reserved vs on-demand modelling and GPU FinOps
GPU, CPU or TPU — matched to the workload
We size compute to what the workload actually does, so you are not paying accelerator prices for jobs a CPU handles well.
GPU
Deep learning training, fine-tuning and high-throughput inference.
NVIDIA data centre and workstation GPUs — DGX and HGX systems, RTX PRO and RTX virtual workstations — on-premise, in colocation or in the cloud.
NVIDIA infrastructureCPU
Data preparation, classical machine learning, vector search and lighter inference.
Not every AI workload needs an accelerator. CPU servers often carry the pipelines, retrieval and smaller models around the GPU estate at lower cost.
TPU
Large-scale training and inference on Google Cloud.
Tensor Processing Units are Google’s AI accelerators, available on Google Cloud. A fit for teams already building on Google Cloud and frameworks such as TensorFlow and JAX.
The whole stack, not a parts list
AI projects stall on what surrounds the accelerators: the power envelope, the fabric, the storage, the scheduling and day-two operations. We own all of it.
Networking fabric
NVLink, NVSwitch, InfiniBand, Spectrum-X and Ethernet fabrics sized for distributed training and inference.
Storage
High-performance storage and data pipelines that keep accelerators fed.
Platform & MLOps
Kubernetes, GPU-aware scheduling, model and artifact repositories, CI/CD and MLOps workflows.
Security & governance
Identity and role-based access, encryption, central logging, usage monitoring and tenant isolation.
Power & cooling
Air-cooled GPU racks typically draw 10–14 kW; dense liquid-cooled racks exceed 40 kW. Assessed before anything is ordered.
Private AI, delivered as a service
On top of the infrastructure, the services that turn GPUs into something the business uses — with your data kept in your environment.
GPU as a Service
Private GPU capacity for training, fine-tuning and inference without running the infrastructure yourself.
How it worksPrivate LLM as a Service
Generative AI on models you control, with secure serving, access control and usage monitoring.
How it worksPrivate RAG as a Service
Permission-aware, source-grounded answers from your own documents and systems.
How it worksPrivate AI API as a Service
One secure, governed API gateway that connects your applications to private models.
How it worksPrivate AI Platform as a Service
Kubernetes, GPU scheduling and MLOps as a ready platform for AI product teams.
How it worksAI governance (ISO/IEC 42001)
A management system for AI risk, accountability and oversight — alongside DPDP data protection.
Read moreFrom assessment to operations
- 01
Assess
Workload profiling, compute sizing (GPU, CPU or TPU), power and cooling survey, and a build-versus-rent cost model.
- 02
Design
Choose the deployment model and reference architecture: compute, fabric, storage, platform, licensing and security.
- 03
Deploy
Supply, rack and validate on-premise or in colocation, or provision on private cloud or a hyperscaler.
- 04
Operate
Monitoring, scheduling, patching, capacity planning and GPU FinOps under an agreed SLA.
AI infrastructure: common questions
Owned hardware, on-premise or in colocation, usually becomes more economical than cloud GPUs when utilisation is sustained above roughly 60–70 percent, or when data residency rules limit where data can be processed. Cloud and hyperscaler capacity suits bursty, experimental and short-lived workloads. Most organisations land on a hybrid split, and Layots models the breakeven point before you commit capital.
GPUs suit deep learning training, fine-tuning and high-throughput inference. CPUs handle data preparation, classical machine learning, vector search and lighter inference at lower cost. TPUs are Google’s accelerators, available on Google Cloud, and suit teams already building on Google Cloud. Many estates use a mix, and the right split depends on the workload.
Yes. Layots designs GPU capacity on Microsoft Azure, AWS, Google Cloud and Oracle Cloud, including TPUs on Google Cloud, and can run hybrid designs that burst from owned hardware into the cloud with consistent tooling.
Where power and cooling already exist, a rack-scale GPU deployment typically takes 6 to 10 weeks from purchase order to first production workload. Projects that need electrical upgrades, liquid cooling or new data centre space usually run 12 to 20 weeks. Cloud and hyperscaler capacity can be provisioned much faster.
An air-cooled GPU rack typically draws 10 to 14 kW, and dense liquid-cooled racks can exceed 40 kW — several times a traditional server rack. Layots runs a power, cooling and floor-loading assessment before any hardware is ordered.
Yes. Private LLM, Private RAG and Private AI API services run models on infrastructure you control, so prompts, documents, embeddings and outputs stay inside your environment, with role-based access, encryption and audit logging.
Insights
More on AI Infrastructure
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Read article Cloud ComputingHow AI Startups Can Scale GPU Compute Without Slowing Innovation
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Read articlePlan Your AI Infrastructure
Tell us about the workload — training, fine-tuning or inference — and we will come back with a sizing, a deployment recommendation and a build-versus-rent view.
- A solution architect reviews your requirement
- We respond, usually within one business day
- You get a practical, costed recommendation
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