COMPUTEGPU / CPU
We start with models, precision and concurrent requests. Usable throughput is validated against the workload.
PRIVATE AI INFRASTRUCTURE
Your AI. Your Data.
Your Infrastructure.
Compute, data and energy designed together. Private infrastructure must serve its workload and work where it will be installed.
Start with requirements01THE INFRASTRUCTURE STACK
Every layer supports the next.
System performance depends on how components work together. We address bottlenecks during design, before installation.
MEMORYVRAM / RAM
Weights, context, cache and runtime all need space. We size memory before selecting the GPU count.
STORAGENVMe / Object / Backup
We separate datasets, models and operational data, defining capacity, concurrent access, retention and recovery.
NETWORK10 / 25 / 100 / 200 / 400 Gb
Speed, topology and interconnects follow data flows. Each network class requires validation of nodes, switches and cabling.
POWERPDU / UPS / BESS / Backup
Continuous load, peaks, runtime and site capacity all inform the electrical design.
COOLINGAirflow / Rack / Liquid
We design airflow and thermal density. Liquid cooling requires compatible components and site infrastructure.
MANAGEMENTMonitoring / Remote / Alerting
Inventory, metrics, access controls and alert thresholds make infrastructure status visible.
02RIGHT-SIZED INFRASTRUCTURE
Scale that fits the use.
Desktop AIThe professional
Local development and a dedicated environment.
Department AIThe team
Shared resources, access controls and internal services.
Enterprise AIThe organisation
Multiple workloads, integrations and operational continuity.
AI RackThe dedicated rack
Compute, networking and power organised into a manageable system.
Micro AI FactoryIntegrated infrastructure
Compute, power and cooling sized as a single installation.
03HOW WE WORK
From requirements to a verified system.
We agree on objectives and acceptance criteria. Sizing and proposals depend on the workload, site and required service level.
- 01 /
Workload
Define what needs to run, on which data and for how many users.
- 02 /
Size
Estimate compute, memory, storage, consumption and growth headroom.
- 03 /
Design
Check compatibility, site conditions, networking, power and cooling.
- 04 /
Deploy
Configure, install and commission against agreed acceptance criteria.
- 05 /
Operate
Define monitoring, maintenance and the scope of support.
- 06 /
Scale
Plan upgrades based on measured usage.
Private AI,
with explicit choices.
Keeping data and models locally can reduce cloud dependency and network latency. Access controls, backups, updates and an operating plan are still required.
We also compare hybrid architectures across initial investment, energy, maintenance, licensing and utilisation to identify the sustainable solution for your workload.
YOUR NEXT STEP