Cisco UCSC-GPU-V100-32 | Tesla V100 32GB HBM2 GPU, 5120 CUDA, 640 Tensor Cores

Q: What workloads is the Tesla V100 32GB best suited for?
A: The V100 32GB excels at AI training, deep learning inference, high-performance computing simulations, and data analytics. Its 640 Tensor Cores accelerate matrix operations for neural networks, while 5120 CUDA cores handle parallel computational tasks efficiently. The 32GB HBM2 memory configuration supports larger datasets and more complex models compared to the 16GB variant.

Q: Which Cisco UCS servers are compatible with this GPU?
A: This GPU is designed for Cisco UCS C-Series rack servers including C240 M5, C240 M5L, and C480 M5 models. It requires a PCIe 3.0 x16 slot, dual-slot clearance, adequate airflow for passive cooling, and sufficient power supply capacity to support the 250W thermal design power.

Q: How does the PCIe version differ from the SXM2 version?
A: The PCIe version uses standard PCI Express 3.0 x16 interface with 32 GB/s bandwidth, making it compatible with most enterprise servers. It features passive cooling and 250W power draw. SXM2 variants offer NVLink interconnect up to 300 GB/s for faster multi-GPU communication, but require specialized server chassis and higher power delivery.

Q: Can multiple V100 GPUs be used together?
A: Yes, multiple V100 GPUs can be deployed in the same server for increased computational power. While the PCIe version supports multi-GPU configurations through PCIe lanes, NVLink technology enables higher-bandwidth GPU-to-GPU communication when using compatible server platforms, significantly improving performance for distributed training and multi-GPU workloads.

Q: What software frameworks are supported?
A: The V100 supports CUDA, DirectCompute, OpenCL, and major deep learning frameworks including TensorFlow, PyTorch, and MXNet. It is compatible with Windows Server 2008 R2 through 2016, Windows 7-10, and Linux distributions with appropriate NVIDIA drivers and CUDA toolkit installed.