Hong Kong GPU server with 2xTesla V100 16GB, 2xEPYC-7502 64 Cores 128 Threads, 128GB RAM. Pre-installed CUDA+PyTorch for AI training, deep learning, LLM fine-tuning, 3D rendering. From ¥11000/mo
Performance specs (RTX 4090 example): FP32 compute 82.6 TFLOPS; Memory bandwidth 1008 GB/s; 16384 CUDA cores; 128 ray tracing cores; 512 Tensor cores. Real performance: BERT training 15x faster, Stable Diffusion 3 seconds per image.
Multi-GPU parallel solutions: 1) Data parallelism: Each GPU processes different batches, most common; 2) Model parallelism: Large models split across GPUs; 3) NVLink: High-speed GPU communication (600GB/s); 4) Distributed training: Supports Horovod, DeepSpeed frameworks. Configuration guidance available.
One-stop environment setup: 1) Base environment: Ubuntu + CUDA + Docker; 2) Python environment: Anaconda + Jupyter; 3) Deep learning frameworks: TensorFlow, PyTorch, JAX; 4) Tool libraries: NumPy, Pandas, Scikit-learn; 5) Optional: Custom environment configuration (paid service).
Data transfer solutions: 1) Network: FTP/SFTP/rsync for small-medium data; 2) Object storage: S3 compatible for cloud data; 3) High-speed: Aspera tools (paid), 10x faster; 4) Physical: Hard drive shipping for TB-scale datasets; 5) Internal: Free high-speed transfer between servers in same data center.