AI infrastructure
AI Infrastructure Intelligence, Engineering & Autonomous Operations
PredictML helps organisations decide what AI infrastructure they need, build it, and run it — from choosing the hardware for a model, through GPU clusters and data centers, to the AI cloud platform and the economics of operating it.
Solutions
From choosing the hardware to running what is deployed on it.
AI Workload, Model & Inference Advisory
Workload discovery, model evaluation, precision and memory planning, and benchmark-driven GPU sizing.
GPU Architecture & Cluster Optimization
GPU architecture choices, NVLink domains, cluster fabrics and scaling, and procurement BOM optimization.
AI Data Center Infrastructure Advisory
Own DC, colocation and hybrid options, high-density racks, liquid cooling, power and facility expansion.
AI Cloud Platform & Deployment Advisory
GPU IaaS, inference PaaS and Model as a Service, private AI clouds, orchestration, and go-live.
Infrastructure Economics, Capacity & Operations
Commercial models, CapEx, OpEx, TCO and ROI, utilization forecasting and capacity expansion.
Token Economics & Agent Optimization
Agent and concurrency modeling, caching, batching and routing, precision and cost per million tokens.
How an engagement runs
Assess, architect, deliver, operate.
- 01
Assess
Understand the workload, its constraints and its budget, and state the assumptions the rest of the work depends on.
- 02
Architect
Design the options, compare them, and recommend one, with the reasons and the risks written down.
- 03
Deliver
Procure, build and deploy the chosen design, and validate it before it carries production work.
- 04
Operate
Monitor, maintain and improve it, within the limits and approvals agreed with you.
Platform
GPUClouds
GPUClouds is PredictML's GPU cloud platform. Its site sets out dedicated GPU compute — on demand, as AI lab workstations, as bare metal and as clusters — together with guidance on sizing an AI workload and services for building AI systems.
- Dedicated GPU compute
- On-demand GPU compute
- AI lab workstations
- Bare-metal GPU servers
- GPU clusters
- AI sizing guidance
Inference economics
Knowing what each AI request costs, and where it should go.
Serving models is a running cost that grows with use. Our workload, model and inference advisory and our token economics work cover which model to run and at what precision, how agents and requests are batched, cached and routed, and the cost per million tokens that results. prouter.io, PredictML's model router, sends each request to a model that suits it.
Next step
Start with the workload.
Tell us what you need to run, where it has to run and what it can cost, and we will set out the options.