Enterprise GPU Power: Escape Hourly Cloud Billing
Cloud GPU instances seem flexible but costs accumulate fast. A100 40GB example: cloud pricing ~$7/hour totals $5,000/month; TooServer dedicated GPU servers start at just $1,650/month—67% cost reduction with identical performance. Dedicated physical GPUs with no overselling, no contention, 24/7 full-load operation at no extra charge.
Ready-to-Use AI Development Environment
All GPU servers come pre-installed with Ubuntu 22.04 + CUDA 12.x + cuDNN 9.x complete stack. TensorFlow, PyTorch, Keras deploy with one click. Supports Docker/Kubernetes containerization and remote Jupyter Notebook development. Skip hours of environment setup—start training immediately. Contact technical team for specialized framework assistance.
Multi-GPU Parallel & VRAM Options on Demand
From single RTX 4090 (24GB VRAM) to multi-card A100 (80GB×8), we offer full GPU configurations:
Entry-Level (RTX 3080/4080): Small-medium model training, inference deployment, graphics rendering—best value.
Professional (RTX 4090/V100): Mainstream AI R&D, Stable Diffusion, LLM fine-tuning—single-card peak performance.
Enterprise (A100/H100): Large-scale distributed training, 100B+ parameter models, scientific computing—unlimited power.
High-Speed Network & Low-Latency Data Transfer
GPU training requires massive dataset loading. Sha Tin datacenter features 10Gbps internal network + NVMe SSD arrays with 3500MB/s read speeds, eliminating I/O bottlenecks. CN2 GIA international routing ensures smooth remote SSH development with Jupyter response latency under 50ms. Supports large data disk expansion for TB-scale local dataset storage.
Professional Cooling for Stable GPU Operation
High-end GPUs draw 450W at full load—inadequate cooling triggers thermal throttling. We deploy cold aisle containment + precision cooling + liquid cooling assist triple thermal architecture, keeping GPU core temperatures stable under 70°C. 24/7 full-load training with zero performance degradation and guaranteed hardware longevity.