The next generation of new p series Flavor Elastic Cloud Servers use NVIDIA Tesla V100 GPUs and provide flexibility, high-performance computing, and cost-effectiveness. These Elastic Cloud Server (ECS) use GPU NVLink for direct communication between GPUs, improving data transmission efficiency. P2v ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.
- Up to eight NVIDIA Tesla V100 GPUs on an ECS
- NVIDIA CUDA parallel computing and common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
- 15.7 TFLOPS of single-precision computing and 7.8 TFLOPS of double-precision computing
- NVIDIA Tensor cores with 125 TFLOPS of single- and double-precision computing for deep learning
- Up to 30 GB/s of network bandwidth on a single ECS
- 16 GB of HBM2 GPU memory with a bandwidth of 900 GB/s
Supported Common Software
- P2v Flavor are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA, use p2v Flavor P2v support the following commonly used software:
- Common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
- CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
- Agisoft PhotoScan
Further information can be found in the ECS area of the Help Center.