AI Infrastructure

Enterprise AI Infrastructure
Global Cloud & GPU Capacity Solutions

Access the compute power your AI ambitions require. We help businesses procure GPU capacity, design multi-cloud strategies and optimise infrastructure costs — without being locked into a single vendor.

01 · GPU Capacity Procurement

Enterprise GPU capacity,
sourced across the APAC region.

We help businesses in Australia and Asia-Pacific procure high-performance GPU capacity for AI training, fine-tuning and large-scale inference — across the chip architectures that matter most in production environments.

H200
High-Memory · 141GB HBM3e
Purpose-built for large language model training and multi-billion parameter inference at scale. NEB AI's primary focus for enterprise AI workloads.
B200
Next-Gen · Blackwell Architecture
NVIDIA's latest architecture delivering up to 4× inference performance over H100. Priority sourcing available for qualified enterprise deployments.
H100
Industry Standard · 80GB HBM2e/HBM3
The proven workhorse for enterprise AI training, fine-tuning and inference. Available across SXM5 and PCIe form factors with NVLink interconnect support.
A100
Established · 40GB / 80GB
Cost-effective and widely available. Ideal for inference workloads, model fine-tuning and businesses scaling into AI without requiring cutting-edge hardware.
Coverage
Australia & APAC
Sourcing and deployment support across Australia, Singapore, Hong Kong, Japan and broader Asia-Pacific cloud regions.
Capacity Model
Reserved, On-Demand & Spot
Flexible capacity structures depending on workload predictability, budget and time-to-resource requirements.
Scale
Test Runs to Enterprise Scale
From initial proof-of-concept GPU clusters through to sustained multi-node enterprise deployments with capacity planning support.
What We Help With

Infrastructure built
around your workload.

Cloud Infrastructure

Cloud Infrastructure

We help businesses design and deploy infrastructure across the major cloud platforms — choosing the right environment for your workload, not just the most familiar one.

AWS AWS
Azure Azure
Google Cloud Google Cloud
Capacity Planning

Capacity Planning

Right-sizing compute for your actual needs — whether that's a one-off training run or sustained inference at scale.

  • AI model training environments
  • Inference deployment at scale
  • Multi-region capacity expansion
AI Training Environments

AI Training Environments

Properly configured environments for training and fine-tuning AI models — from single-GPU experiments to distributed multi-node clusters.

  • Environment setup and configuration
  • Storage and data pipeline design
  • Scalable from prototype to production
Cost Optimisation

Cost Optimisation

AI infrastructure spend can spiral quickly. We help businesses find the most cost-effective path without compromising on performance.

  • Reducing GPU compute costs
  • Cloud resource cost optimisation
  • Right-sizing and utilisation review
How It Works

From requirement
to running infrastructure.

01

Requirements Call

We understand your workload, timeline and budget constraints.

02

Capacity Sourcing

We identify the right GPU and cloud resources for your specific need.

03

Deployment

Infrastructure is configured, tested and handed over ready to use.

04

Ongoing Optimisation

We monitor usage and continually look for cost and performance gains.

Get Started

Request an Infrastructure
Consultation

Tell us about your AI workload and we'll help you find the right compute and cloud strategy — no vendor bias, no lock-in.

Request Infrastructure Consultation