AI GPU Systems

Performance Engineered for Your Workload

GPUs are the computational core of modern AI infrastructure. But performance is not defined by the GPU model alone. It depends on memory capacity, bandwidth, interconnect topology, and system balance.

DiGiCOR designs GPU systems tailored to AI training, inference, generative AI, and high-performance computing workloads.

Key Considerations

Why GPU Architecture Matters

Selecting a GPU is only the first step.

Effective AI performance depends on:

GPU memory size
Memory bandwidth
Inter-GPU communication
PCIe lane availability
CPU pairing
Storage throughput
Power and cooling capacity

The Bottleneck Risk

An unbalanced system can bottleneck even the most powerful accelerator.

Our Approach

We design GPU systems where every component works together to maximise throughput.

Performance Metrics

Memory & Bandwidth Comparison

Why Specifications Matter

AI performance is often constrained by memory and data movement — not raw compute alone.

Key factors include:

GPU memory capacity (VRAM)
Memory bandwidth
Interconnect speed
GPU-to-GPU communication
CPU-to-GPU data transfer

Why This Impacts You

→Larger models require more VRAM
→Training speed is affected by bandwidth
→Multi-GPU scaling depends on interconnect efficiency
→Inference throughput varies by memory architecture

Our Evaluation

We evaluate these parameters in relation to:

Model size
Batch size
Dataset complexity
Expected concurrency

This ensures infrastructure is designed for sustained performance, not theoretical peak metrics.

GPU Landscape

Datacenter GPU Comparison

Blackwell vs Hopper vs MI300X — A clear comparison of today's leading datacenter accelerators for large-scale AI training and low-latency inference.

FLOPs depend on precision (FP4/FP8/FP16/FP32) and sparsity; memory and bandwidth vary by SKU and system.

Family	Peak AI FLOPs	Memory per GPU	Interconnect	Ideal Use Case
NVIDIA Blackwell (GB200 / GB300)	FP4/FP6 acceleration via second-gen Transformer Engine; designed for real-time trillion-parameter LLM inference at rack scale	HBM3e per GPU. Full NVL72 rack: ~13.5 TB HBM3e across 72 GPUs	NVLink 5 ≈1.8 TB/s per GPU; NVLink Switch fabric ≈130 TB/s per rack	Ultra‑low latency inference and unified NVLink‑domain training at extreme scale.
NVIDIA Hopper (H100 / H200)	Petaflop-class Tensor performance in FP8/FP16 using Transformer Engine (config-dependent)	H200: ~141 GB HBM3e with ~4.8 TB/s bandwidth (SXM)	NVLink 4 ≈0.9 TB/s peer-to-peer bandwidth per GPU	Proven, widely deployed platform for training and inference with a mature CUDA ecosystem.
AMD Instinct MI300X (CDNA 3)	Petaflop-class tensor capability in FP8/BF16/FP16 (config-dependent)	~192 GB HBM3 per GPU; 8-GPU baseboard: ~1.5 TB HBM	Infinity Fabric within module; PCIe Gen5 to host (OAM)	Best where models are memory‑bound and benefit from very large per‑GPU memory.

Fastest Inference

Choose Blackwell: NVLink-5 + NVLink Switch form a unified, high-bandwidth GPU domain for ultra-low latency at scale.

Production Stability

Hopper H200 is a proven workhorse with high memory bandwidth, broad framework support, and mature CUDA ecosystem.

Maximum Memory

MI300X offers ~192 GB HBM3 per GPU and ~1.5 TB per 8-GPU baseboard for memory-intensive workloads.

Software & Ecosystem

NVIDIA (Blackwell & Hopper)

• CUDA toolchain: cuDNN, TensorRT / TensorRT-LLM, Triton
• Enterprise support: NVIDIA AI Enterprise across generations
• Scaling: Blackwell → NVLink-5 + Switch; Hopper → NVLink-4

AMD (MI300X)

• ROCm stack: HIP, math libraries, framework builds
• Use cases: Training and inference workloads
• Scaling: High HBM per GPU with host fabrics for multi-node

Infrastructure Planning

Workload Matching Guide

Choosing the Right GPU System — Different workloads benefit from different configurations.

AI Model Training

Multi-GPU nodes
High-memory GPUs
High-speed networking
Balanced storage architecture

AI Inference

Right-sized GPU or CPU systems
Low-latency optimisation
Energy-efficient deployments

Generative AI / LLM Workloads

High-memory GPUs
NVLink-enabled configurations
Scalable architecture for growth

Edge AI

Compact GPU systems
Power-efficient configurations
Rugged or embedded options

Our Philosophy: We guide organisations through structured evaluation rather than defaulting to the highest-tier hardware. The goal is performance efficiency — not overprovisioning.

Holistic Design

System Integration Considerations

GPU systems must be engineered holistically.

Power Density Planning

Optimize electrical infrastructure for sustainable performance

Thermal Management

Maintain optimal operating temperatures under load

Rack Integration

Seamless deployment in standard data centre environments

Expansion Capacity

Built-in flexibility for adding GPU nodes and storage

Long-term Upgrade Paths

Scale infrastructure without requiring full redesign

Infrastructure Philosophy

Infrastructure should scale without requiring full redesign. We plan for evolution, not replacement.

Every design decision accounts for future growth and changing workload demands.

Your investment in GPU infrastructure remains relevant and extensible for years to come.

GPU System Solutions

Featured GPU Systems

Engineered configurations for every AI workload, from training to inference.

GIGABYTE

Gigabyte R164-SG5-AAV1 Rack Server - Intel® Xeon® 6 1U UP 1 x PCIe Gen5 GPU

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Form Factor: 1U
CPU Sockets: Single
Platform: Intel Xeon 6700/6500 series processors with P-cores
DIMM Slots: 12
Memory Generation: DDR5
Max Number of Drives: 12
No. of Onboard Network Ports: 1
Total Power (W): 1600

NZ$25,503.05 RRP Ex GST

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GIGABYTE

Gigabyte R263-ZG0-AAL2 - AMD EPYC™ 9005/9004 - 2U UP 4 x PCIe Gen5 GPUs

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Form Factor: 2U
CPU Sockets: Single
Platform: AMD EPYC 9005/9004 Series processors
DIMM Slots: 24
Memory Generation: DDR5
Max Number of Drives: 12
No. of Onboard Network Ports: 1
Max Network Throughput (Gb/s): 1Gb/s
Total Power (W): 2700

NZ$18,658.39 RRP Ex GST

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GIGABYTE

Gigabyte XL43-ZX0-AAS2 NVIDIA MGX™ Server - Dual AMD EPYC 8 PCIe Gen5 x GPUs

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Form Factor: 4U
CPU Sockets: Dual
Platform: AMD EPYC 9005/9004 Series processors
DIMM Slots: 24
Memory Generation: DDR5
Max Number of Drives: 4
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 3200

NZ$45,266.86 RRP Ex GST

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GIGABYTE

GIGABYTE XV24-SU0-AAJ1 - NVIDIA MGX™ AI & HPC Server for Next-Gen Workloads

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Form Factor: 2U
CPU Sockets: Single
Platform: Intel Xeon 6700 series processors with E-cores
DIMM Slots: 16
Memory Generation: DDR5
Max Number of Drives: 2
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 2000

NZ$24,564.34 RRP Ex GST

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GIGABYTE

GIGABYTE XV23-ZU0-AAJ1 - NVIDIA MGX™ AI & HPC Server

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Form Factor: 2U
CPU Sockets: Single
Platform: AMD EPYC 9005/9004 Series processors
DIMM Slots: 12
Memory Generation: DDR5
Max Number of Drives: 2
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 2000

NZ$24,005.30 RRP Ex GST

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Asus

ASUS ESC8000A-E13P GPU Server

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CPU Sockets: Dual
Platform: AMD EPYC 9005/9004 Series processors
DIMM Slots: 24
Memory Generation: DDR5
Max Number of Drives: 8
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 3200

NZ$58,283.55 RRP Ex GST

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Supermicro

GPU A+ Server AS -5126GS-TNRT

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CPU Sockets: Dual
Platform: AMD EPYC 9005/9004 Series processors
Maximum RAM Capacity (GB): 6000
Memory Generation: DDR5
Max Number of Drives: 4
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 2700

NZ$94,395.61 RRP Ex GST

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Supermicro

GPU SuperServer SYS-421GE-TNRT

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Form Factor: 4U
CPU Sockets: Dual
Platform: Intel 4th/5th Generation Xeon Scalable Processors
DIMM Slots: 32
Memory Generation: DDR5
Max Number of Drives: 8
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 2700

NZ$49,296.50 RRP Ex GST

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Asus

ASUS ESC4000A-E12 2U GPU Server

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Form Factor: 2U
CPU Sockets: Single
Platform: AMD EPYC 9005/9004 Series processors
DIMM Slots: 12
Memory Generation: DDR5
Max Number of Drives: 6
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 1Gb/s
Total Power (W): 2600

NZ$21,243.97 RRP Ex GST

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Supermicro

GPU SuperServer SYS-741GE-TNRT

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Form Factor: 4U, Tower
CPU Sockets: Dual
Platform: Intel 4th/5th Generation Xeon Scalable Processors
DIMM Slots: 16
Memory Generation: DDR5
Max Number of Drives: 8
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 2000

NZ$46,385.14 RRP Ex GST

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Supermicro

GPU A+ Server SYS-4125GS-TNRT

Show specifications

Form Factor: 4U
CPU Sockets: Dual
Platform: AMD EPYC 9005/9004 Series processors
DIMM Slots: 24
Memory Generation: DDR5
Max Number of Drives: 6
No. of Onboard Network Ports: 2
Max Network Throughput (Gb/s): 10Gb/s
Total Power (W): 2000

NZ$40,109.49 RRP Ex GST

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Design a GPU System
Built for Performance

Whether you're deploying a development workstation or building a multi-node AI cluster, we design GPU systems tailored to your workload and growth strategy.

Send Us a Message

Explore Product Range

AI GPU Systems

Why GPU Architecture Matters

Effective AI performance depends on:

The Bottleneck Risk

Our Approach

Memory & Bandwidth Comparison

Why Specifications Matter

Why This Impacts You

Our Evaluation

Datacenter GPU Comparison

Fastest Inference

Production Stability

Maximum Memory

Software & Ecosystem

NVIDIA (Blackwell & Hopper)

AMD (MI300X)

Workload Matching Guide

AI Model Training

AI Inference

Generative AI / LLM Workloads

Edge AI

System Integration Considerations

Power Density Planning

Thermal Management

Rack Integration

Expansion Capacity

Long-term Upgrade Paths

Infrastructure Philosophy

GPU System Solutions Featured GPU Systems Engineered configurations for every AI workload, from training to inference.

Featured GPU Systems

Design a GPU System Built for Performance

Send Us a Message

Explore Product Range

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Success Stories

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GPU System Solutions

Featured GPU Systems

Engineered configurations for every AI workload, from training to inference.

Design a GPU System
Built for Performance