Enterprise AI Infrastructure

Build the infrastructure your AI ambitions require.

We size, source, and stand up enterprise GPU infrastructure — from a single server to a liquid-cooled, rack-scale cluster — for teams building private AI, inference at scale, and serious research.

One team carries the project from workload assessment through architecture, delivery, and the years of operation after — not a hardware order dropped at your door.

Availability and delivery depend on configuration, country, export compliance, OEM allocation, and project requirements.

Where we start

Start with the workload, not the GPU name.

The right system depends on what it needs to do — not on the model name printed on the box.

  • Training versus inference
  • Model size
  • Context and concurrency
  • Data volume
  • Latency objectives
  • Precision requirements
  • Deployment location
  • Privacy and sovereignty needs
  • Scale-up versus scale-out
  • Power and cooling capacity
  • Operating model
  • Budget and expansion path

Platform families

NVIDIA and AMD accelerated computing platforms

Platform options selected through workload assessment — presented as families, not a catalogue to browse.

Blackwell Ultra

Rack-scale AI system

GB300 NVL72

Rack-scale NVIDIA Blackwell Ultra platform for large-scale training and sovereign AI, available in configurations from Supermicro, Pegatron, and Gigabyte.

Workloads
Large-scale AI training · Advanced inference · Sovereign AI · Research clusters
Form factors
Rack-scale (NVL72)
Cooling
Direct liquid cooling (required for some rack-scale configurations)
Manufacturers
Supermicro · Pegatron · Gigabyte
Listed specifications
Gigabyte configuration lists 72 GB300 GPUs plus 36 Grace CPUs · Listed capacity: 288 GB HBM3e per GPU

Eight-GPU server

B300 NVL8

Eight-GPU NVIDIA Blackwell Ultra server for high-density training and inference, with both air-cooled and liquid-cooled configurations in the supplied inventory.

Workloads
Large-scale AI training · Advanced inference · High-density enterprise compute
Form factors
8-GPU server (NVL8)
Cooling
Air-cooled options present · Direct liquid cooling options present
Manufacturers
Supermicro · Dell

Blackwell

Rack-scale AI system

GB200

NVIDIA Blackwell rack-scale platform for training and inference at scale, available from Dell in the supplied inventory.

Workloads
Model training · Advanced inference · Research clusters
Form factors
Rack-scale
Cooling
Configuration-dependent — confirm with an infrastructure specialist
Manufacturers
Dell

Eight-GPU HGX server

B200 HGX

NVIDIA Blackwell HGX server for enterprise training and inference, available from Lenovo, Supermicro, and Gigabyte with both air- and liquid-cooled options.

Workloads
Model training · Advanced inference · Enterprise AI
Form factors
HGX (8-GPU)
Cooling
Lenovo ThinkSystem SR780a V3 with Neptune direct liquid cooling · Air-cooled options present
Manufacturers
Lenovo · Supermicro · Gigabyte
Listed specifications
Listed capacity: 180 GB per GPU

Hopper

Eight-GPU server

H200

NVIDIA Hopper H200 server for enterprise-scale inference and training, including Dell's PowerEdge XE9680 with HGX H200.

Workloads
Enterprise AI · Advanced inference · Model training
Form factors
8-GPU server · HGX
Cooling
Air-cooled and direct-liquid-cooled configurations
Manufacturers
Supermicro · Dell · Gigabyte
Listed specifications
Dell PowerEdge XE9680 with HGX H200 · Listed capacity: 141 GB per GPU

GPU server

H100

NVIDIA Hopper H100 in SXM5, PCIe, HGX, and NVL form factors, from Supermicro, Dell, HPE, and ASUS, with air- and liquid-cooled options.

Workloads
Enterprise AI · Inference · Model training
Form factors
SXM5 · PCIe · HGX · NVL
Cooling
Air-cooled and direct-liquid-cooled configurations
Manufacturers
Supermicro · Dell · HPE · ASUS

Ada Lovelace

Inference and visual-computing system

L40S

NVIDIA Ada Lovelace L40S platform for generative AI inference, rendering, simulation, and digital twins — presented as a platform family selected through workload assessment, not a fit for every workload.

Workloads
Generative AI inference · Enterprise AI · Rendering · Simulation · Digital twins · Visual computing
Form factors
GPU server
Cooling
Configuration-dependent — confirm with an infrastructure specialist
Manufacturers
Supermicro
Listed specifications
Supermicro configuration lists ten L40S GPUs

AMD Instinct

Alternative accelerator platform

MI300X

AMD Instinct MI300X eight-GPU server — an alternative accelerator platform, not positioned as exclusive to any single vendor.

Workloads
Model training · Inference · High-performance computing
Form factors
8-GPU server
Cooling
Configuration-dependent — confirm with an infrastructure specialist
Manufacturers
Supermicro

Beyond the GPU

The complete solution stack

Compute is one layer. We design and integrate the rest — networking, storage, platform software, cooling, and power.

Compute

GPU servers and rack-scale platforms sized to the workload.

  • GPU servers
  • Rack-scale platforms
  • Scale-up and scale-out architectures
  • CPU and accelerator selection

Networking

Fabric design matched to how the cluster actually communicates.

  • High-speed Ethernet
  • InfiniBand where appropriate
  • East-west fabric design
  • Cluster topology
  • Management networks

Storage

From training data to checkpoints, sized and tiered correctly.

  • Training-data storage
  • Checkpoint storage
  • High-throughput shared storage
  • Object storage
  • Backup and lifecycle planning

Platform software

Design and integration capabilities — not automatically included licences.

  • Kubernetes
  • Slurm
  • Containers
  • Scheduling
  • Observability
  • Private model serving
  • Access control

Cooling and power

COLOPiO's energy-system experience applied to the machine room, not just the rack.

  • Air-cooled systems
  • Direct liquid cooling
  • Rack power assessment
  • UPS and battery backup
  • Generator integration
  • Energy monitoring
  • Capacity planning

Services

One engineering partner from discovery through operation.

  • Discovery and workload assessment
  • Architecture and bill of materials
  • Procurement and logistics
  • Installation and commissioning
  • Platform integration
  • Acceptance testing
  • Training and handover
  • Monitoring and support
  • Capacity expansion

How a deployment happens

Discovery to operation

The same disciplined path for every deployment, from first workload conversation to expansion planning.

AI infrastructure deployment processDiscoverworkload assessment01Sizecapacity + configuration02Architectcompute, network, storage03Deliverprocurement + logistics04Commissioninstall + acceptance test05Operate and expandmonitor, support, grow06
AI infrastructure deployment process· swipe →

Who this is for

Built for organizations deploying serious AI

Sovereign and public-sector AITelecom operatorsUniversities and research institutionsFinancial servicesLegal and professional servicesOil and gasManufacturingMediaCloud and managed-service providersEnterprises deploying private AI

Manufacturer ecosystem

Platform options are available from leading enterprise system manufacturers, including Supermicro, Dell, Gigabyte, Lenovo, HPE, ASUS, and Pegatron, subject to configuration and availability.

Tell us what you need the system to do.

Share the workload, users, model profile, deployment location, security requirements, timeline, and expected growth. COLOPiO will translate those requirements into an infrastructure architecture and commercial proposal.