Cisco UCSC-GPUV100SXM32 | Tesla V100 GPU Accelerator, 32GB HBM2, SXM2 300W

Q: What is the difference between the SXM2 and PCIe versions of the Tesla V100?
A: The SXM2 version (UCSC-GPUV100SXM32) features a 300W TDP and supports NVLink 2.0 interconnect for up to 300GB/s GPU-to-GPU bandwidth, enabling up to 8 GPUs to be interconnected in a single server. The PCIe version (UCSC-GPU-V100-32=) has a 250W TDP and uses PCIe Gen3 x16 for 32GB/s bandwidth. The SXM2 form factor delivers higher performance and is designed for multi-GPU HPC and AI training workloads.

Q: Which Cisco servers support this SXM2 GPU accelerator?
A: The UCSC-GPUV100SXM32 is compatible with Cisco UCS C-Series rack servers that feature SXM2 GPU support, including the C240 M5 with Riser 2A configuration and the C480 M5 Standard Base Chassis. These platforms are designed for high-density GPU deployments with appropriate power and cooling infrastructure to support multiple 300W SXM2 modules.

Q: What workloads benefit most from the V100's Tensor cores?
A: The 640 Tensor cores in the V100 accelerate mixed-precision matrix operations critical for deep learning training and inference, particularly neural network training with FP16 and INT8 precision. Applications include image classification, natural language processing, recommendation systems, and generative AI models. For traditional HPC workloads like molecular dynamics or computational fluid dynamics, the 5,120 CUDA cores provide strong FP64 and FP32 performance.

Q: How does NVLink 2.0 improve multi-GPU performance?
A: NVLink 2.0 provides up to 300GB/s bidirectional bandwidth between V100 GPUs, nearly 10x faster than PCIe Gen3 x16. This enables efficient scaling of workloads across multiple GPUs with minimal data transfer bottlenecks, ideal for large-scale deep learning model training, multi-GPU simulations, and distributed computing tasks that require frequent inter-GPU communication.

Q: Can this GPU be used for virtualization or VDI deployments?
A: While the V100 supports NVIDIA vGPU software for virtualization, it is primarily optimized for compute workloads rather than VDI. For high-density VDI deployments, NVIDIA Tesla M10 or T4 GPUs are more cost-effective. The V100 excels in AI training, HPC simulations, and inference workloads where Tensor core acceleration and high memory bandwidth are critical.