Brisbane Enterprise GPU Nodes: Purpose-Built for AI & Deep Learning

The rapid advancement of AI demands unprecedented compute power. Our Brisbane data center offers Dedicated GPU Servers equipped with the latest NVIDIA professional cards (including RTX 4090, RTX 4080, and A100). The RTX 4090, for instance, features 16,384 CUDA cores delivering up to 82.6 TFLOPS of computing power. Beyond top-tier hardware, we provide pre-installed Ubuntu environments fully configured with CUDA, cuDNN, and TensorRT, fully compatible with TensorFlow and PyTorch for immediate deployment. Compared to expensive hourly cloud GPU instances, our bare-metal GPU servers slash costs by up to 66% (1/3 the price), making them the most cost-effective solution for AI startups, research institutions, and 3D rendering studios.

Brisbane1 Options
CPUGPURAMStorageBandwidthIPPricingActions
2xGold-5515 32 Cores 64 ThreadsNvidia A2 16GB256GB1000GB SSD1GB@10TB1IP ¥6800 Deploy Now
Australia Servers - 22 OptionsAustralia VPS Hosting - 4 Options
India Servers | Mumbai Native IP | From ¥999/mo
native India IP. 100% dedicated hardware, 99.9% uptime, 24/7 support.
Netherlands Dedicated Servers | Amsterdam Native IP | From ￥299/mo
Netherlands dedicated servers in Amsterdam Tier3+ data centers with native Netherlands IP. 7 configs from ￥299/mo.
Canada Dedicated Servers | Montreal Native IP | From ￥699/mo
Canada dedicated servers in Montreal Tier3+ data centers with native Canada IP. 4 configs from ￥699/mo.

GPU Server Purchasing Guide & Cooling Architecture

Unleashing the full potential of a GPU server requires more than just a high-end graphics card; it demands a balanced system architecture and robust data center cooling. Our architects recommend focusing on these core elements:

  • ✅ Eliminating System Bottlenecks: CPU must be robust (Intel Xeon or AMD EPYC recommended). System RAM should be 2-4x the GPU VRAM, and NVMe SSDs are strictly required to eliminate I/O latency when loading massive training datasets.
  • ✅ Enterprise-Grade Cooling: High-end GPUs consume massive power. Our Brisbane facility utilizes high-density cold aisle containment and precision cooling systems to ensure zero thermal throttling, guaranteeing 100% performance output and extending hardware lifespan.
  • ✅ Matching GPU to Workload: The RTX 4090 offers unmatched value for small-to-medium LLM training, inference, and video rendering. The A100, with its massive memory bandwidth, is the undisputed choice for ultra-large AI models and mixed-precision computing.

💰 From ¥3999 / Month. A fraction of the cost of public cloud GPU instances, with heavy discounts for annual plans.
View Brisbane GPU Server Configurations Here →

Australia GPU Server FAQ

Do you offer a free trial for Australia servers?

Free trials are not available for dedicated servers. However, we provide Test IPs and a Looking Glass tool for you to benchmark latency and routing paths.<br>We recommend starting with a monthly plan for a low-cost trial. A full refund is guaranteed in case of any hardware failure.

Payment methods & Refund policy?

1. We accept Alipay, WeChat Pay, PayPal, and USDT (TRC20).<br>2. We do not support unconditional refunds. However, in the rare event of hardware failure or unresolvable network issues, we guarantee a prorated refund based on the remaining service days.

What can GPU servers do? What uses are prohibited?

Suitable for: AI training/inference, deep learning, cloud gaming, 3D rendering, video transcoding, scientific computing. PROHIBITED: Cryptocurrency mining is strictly forbidden - violations result in immediate termination without refund. Other illegal uses also prohibited.

Is hourly or monthly rental more cost-effective for GPU servers?

Depends on usage duration: 1) Short tasks (<7 days): Hourly more flexible and economical; 2) Long-term (>15 days/month): Monthly cheaper, typically saves 40-60%; 3) Uncertain duration: Start hourly to test, switch to monthly when confirmed. Billing methods can be switched anytime, annual payment discounts available.

Does GPU server support CUDA? Is deep learning environment pre-installed?

Full CUDA support. Pre-installation available: CUDA 11.8/12.0, cuDNN 8.9, TensorRT 8.6; Python/Anaconda environment; TensorFlow, PyTorch, JAX frameworks; Docker GPU support (nvidia-docker). Custom versions available - ready to use out of the box.

Can GPU servers train large language models?

Large model training capacity: Single card: 7B parameters (RTX 4090/A100 40GB), 13B (A100 40GB), 30B (A100 80GB); Multi-card: 70B (4x A100), 175B (8x A100). Model parallelism and pipeline parallelism supported. Configuration recommendations based on model size available.