Sha Tin GPU Servers & Dedicated Tesla V100 16GB Hosting

20MbpsBandwidth
99.9%SLA Guarantee
1 HourFastest Delivery

Tech Specs Back to Listings

Data Center LocationsAsia>Hong Kong>Sha Tin
CPU2xEPYC-7302 32 Cores 64 Threads
RAM64GB
Storage960GB SSD
BandwidthCN2-20M · 20Mbps
IP Address 3 IPs
GPUTesla V100 16GB
Use Casesdatabase clusters, virtualization platforms, and high-concurrency applications
GPU Servers
Tesla/RTX Enterprise GPU, 80GB VRAM 312TFLOPS
V100/A100/4090
CUDA Ready
LLM Training
3D Rendering

Sha Tin GPU Server FAQ

How is the computing performance of Tesla V100 16G GPU servers?

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.

How to do parallel training with multiple GPUs?

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.

How to configure deep learning training environment?

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).