Hong Kong · USA
NVIDIA RTX/Tesla Graphics Cards · High Performance Computing · NVMe SSD Storage · Low Latency Direct Connection
Ideal for AI Training · Deep Learning · Graphics Rendering · Video Processing · Minute-Level Activation
2 regional nodes with NVIDIA professional graphics cards to meet AI and graphics computing needs
NVIDIA RTX/Tesla Graphics Cards
High Bandwidth & Performance
Professional GPU computing platform with enterprise-grade performance to accelerate your AI and graphics projects
Equipped with NVIDIA RTX and Tesla series professional graphics cards, powerful CUDA cores and Tensor cores
Supports CUDA, TensorFlow, PyTorch, and other mainstream AI frameworks with excellent computing performance
Pre-installed deep learning environment, activated in 10 minutes, ready to use immediately
Encrypted data storage, regular backups, 99.9% uptime SLA guarantee
Support on-demand upgrades for GPU, CPU, and memory configurations with hourly or monthly billing
GPU expert team available around the clock, providing technical consulting and problem resolution
GPUs deliver 10-100x speedup over CPUs for parallel computing tasks. Real benchmarks: ResNet-50 training takes 10 days on 8-core CPU vs 6 hours on RTX 4090; GPT-class LLMs are virtually impossible on CPU but train in days on A100 clusters. GPUs excel through thousands of CUDA cores executing tensor operations simultaneously—perfectly aligned with deep learning's matrix math. Ideal applications: computer vision (object detection, segmentation), NLP (transformers, LLMs), generative AI (Stable Diffusion, GANs), recommendation systems, and scientific simulation.
RTX 4090: 24GB GDDR6X, exceptional price/performance, ideal for startups, research teams, individual developers. $1,800-2,500/month. Tesla A100: 40GB/80GB HBM2 with ECC, enterprise reliability, production AI training and inference at scale. $460-1,200/month. H100: 80GB HBM3, 4th-gen Tensor Cores, Hopper architecture, for frontier models (100B+ parameters) and research institutions. $3,000+/month. Budget-conscious: RTX. Mission-critical production: A100. Cutting-edge research: H100. Multi-GPU configs available for all tiers.
Support 2-8 GPU configurations with PyTorch/TensorFlow native distributed training. NVLink advantage: 600GB/s inter-GPU bandwidth (NVLink 4.0), 10x faster than PCIe 4.0, dramatically reducing multi-GPU communication overhead. Training strategies: 1) Data parallelism: split batches across GPUs, near-linear scaling; 2) Model parallelism: partition large models across devices; 3) Pipeline parallelism: assign layers to different GPUs. Recommended: 4x A100 NVLink config balances performance and cost for most production workloads. We handle topology optimization and driver tuning.
Two enterprise images: 1) Clean OS (Ubuntu 22.04 LTS/CentOS Stream): for teams with custom environment requirements; 2) ML Workstation (pre-installed: CUDA 12.2, cuDNN 8.9, latest NVIDIA drivers, PyTorch 2.1, TensorFlow 2.14, JAX, Jupyter Lab, VS Code Server, data science stack), ready out-of-box. Custom images available for enterprise: specific framework versions, pre-loaded models, proprietary toolchains. Docker and Kubernetes orchestration supported for containerized workflows.
Flexible billing: 1) On-demand hourly: pay-as-you-go for experiments, debugging, coursework—RTX 4090 ~$0.8-$1/hour; 2) Monthly reserved: long-term development, 20-30% discount vs hourly; 3) Committed use: 3-month/1-year contracts, additional 15-25% off; 4) Spot instances: preemptible idle capacity at 50-70% discount, ideal for fault-tolerant training with checkpointing. Cost controls: budget alerts, auto-shutdown policies, scheduled scaling. Enterprise volume discounts negotiable for sustained usage >$10K/month.
Generative AI: Stable Diffusion image synthesis, LLaMA/Mistral/GPT enterprise chatbots, text generation, voice synthesis. Computer Vision: facial recognition, YOLO object detection, medical imaging diagnostics, industrial quality inspection, autonomous vehicle perception. NLP: sentiment analysis, document classification, intelligent customer service, machine translation, retrieval-augmented generation (RAG). Recommender Systems: e-commerce personalization, content recommendations, ad targeting optimization. Scientific Computing: drug discovery molecular simulation, climate modeling, financial risk analysis. We provide industry solution consulting and architecture reviews.
Network: Hong Kong/APAC GPUs offer 100Mbps-1Gbps—uploading 100GB takes 15min-3hrs. Recommended workflow: 1) Object storage staging: upload datasets to our S3-compatible storage first, servers download via 10Gbps internal network (minutes for TB-scale); 2) Data pre-deployment: we pre-load TB datasets to local NVMe arrays before handoff; 3) Incremental sync: rsync/rclone for updates. Local storage: each GPU server includes 1-4TB NVMe SSD (expandable to 10s of TB), plus optional network-attached storage for shared datasets across GPU clusters.
Multi-layer protection: 1) Framework checkpoints: PyTorch/TensorFlow auto-save model weights every N steps; 2) System snapshots: daily automated backups, 7-30 day retention; 3) Remote replication: sync critical models/data to object storage or cross-region backup; 4) Hardware resilience: enterprise GPUs + ECC memory + RAID 10 + UPS power, <0.1% failure rate. In rare outage scenarios, resume from latest checkpoint—typical loss <1 hour training. Monitoring/alerting via Slack/PagerDuty/email. Enterprise SLA includes replacement hardware and data recovery assistance.