NVIDIA Tesla V100S 32GB PCIe Gen3 Passive GPU for HPE ProLiant DL380 Gen10

The NVIDIA Tesla V100S 32GB PCIe Gen3 Passive GPU (HPE Part Number P11785-B21) delivers Volta-generation GPU computing for HPE ProLiant DL380 Gen10 servers, combining 32GB of HBM2 memory with 900 GB/s memory bandwidth and 16.4 TFLOPS of FP32 single-precision compute performance. Built on NVIDIA's Volta architecture with 5,120 CUDA cores and 640 Tensor Cores, it accelerates deep learning training and inference workloads at scale, while its NVLink-compatible design and PCIe Gen3 x16 host interface ensure broad compatibility within HPE's validated server ecosystem.

The V100S extends the original V100 platform with higher memory capacity and increased memory bandwidth, making it particularly well-suited for large-model training, high-resolution simulation, and memory-intensive HPC applications. The passive cooling configuration is engineered for deployment in data center rack environments where chassis airflow is managed by the host server, keeping acoustic and thermal profiles aligned with enterprise operations center requirements. HPE factory integration ensures that firmware, drivers, and thermal design are validated against the ProLiant DL380 Gen10 platform.

As an HPE-branded option, the P11785-B21 is supported through HPE's Integrated Lights-Out (iLO) management infrastructure, enabling health monitoring, alerting, and lifecycle management consistent with the broader ProLiant server environment. The card supports CUDA, OpenCL, and DirectCompute frameworks, and is compatible with major AI frameworks including TensorFlow, PyTorch, and MXNet, as well as HPC toolchains leveraging OpenACC and OpenMP GPU offloading.

Ideal for

• Large-scale deep learning model training for computer vision, natural language processing, and recommendation system workloads in enterprise AI research environments
• High-performance computational fluid dynamics, molecular dynamics, and finite element analysis simulations requiring high memory bandwidth and double-precision throughput
• Inference acceleration for deployed AI models in data center environments where low-latency, high-throughput batch inferencing is required at scale
• Seismic processing and reservoir simulation in energy sector HPC clusters demanding sustained FP64 performance and large memory footprints
• Financial risk modeling and quantitative analytics workloads, including Monte Carlo simulations and portfolio optimization, where GPU-accelerated compute reduces time-to-insight
• Medical imaging and genomics data processing pipelines requiring high memory capacity to handle large dataset batches without frequent host-to-device memory transfers

Technical specifications

Available from Omnixon Global. Submit an RFQ and our team will confirm configuration and availability for your order.