NVIDIA Tesla V100 32GB SXM2 GPU for HPE Apollo 6500 Gen10

The NVIDIA Tesla V100 32GB SXM2 GPU for HPE Apollo 6500 Gen10 (P11786-B21) is a high-performance accelerator built on NVIDIA's Volta GV100 architecture, purpose-engineered for demanding AI, deep learning, and high-performance computing workloads in enterprise and hyperscale datacenter environments. The SXM2 form factor enables NVLink 2.0 interconnect support, delivering significantly higher GPU-to-GPU bandwidth than PCIe-based alternatives, which is critical for multi-GPU training runs and tightly coupled parallel workloads.

The accelerator provides 32GB of HBM2 (High Bandwidth Memory) with a memory bandwidth of 900 GB/s, and delivers up to 125 teraflops of deep learning performance with Tensor Cores. The V100 Tensor Core architecture accelerates mixed-precision matrix operations (FP16/FP32), enabling dramatically faster training of large neural networks. With 5,120 CUDA cores and 640 Tensor Cores, the card also supports FP64 workloads at 7.8 TFLOPS, making it equally capable for traditional scientific and engineering simulation workloads.

Factory-integrated and validated specifically for the HPE Apollo 6500 Gen10 platform, P11786-B21 ensures compatibility with HPE's dense GPU server chassis, which supports up to eight GPU accelerators in a 4U form factor. This integration provides optimized thermal management, power delivery, and system-level firmware compatibility, supporting enterprise IT teams with a solution that is ready for production deployment in AI research, genomics, computational fluid dynamics, financial analytics, and large-scale data science pipelines.

Ideal for

• Large-scale deep learning model training, including computer vision, natural language processing, and recommendation systems requiring high Tensor Core throughput and multi-GPU NVLink scaling
• High-performance computing simulations in scientific research, including computational fluid dynamics, molecular dynamics, and climate modeling that depend on strong FP64 double-precision performance
• AI inference at scale for enterprise applications requiring low-latency, high-throughput GPU-accelerated inference on trained neural network models
• Genomics and life sciences workloads involving genome sequencing, protein structure analysis, and drug discovery pipelines that benefit from high memory bandwidth and large GPU memory capacity
• Financial services quantitative computing, including Monte Carlo simulations, risk analytics, and real-time fraud detection models that require GPU-accelerated parallel processing
• Big data analytics and in-memory data science workloads using GPU-accelerated frameworks such as RAPIDS, enabling faster ETL, feature engineering, and exploratory analysis on enterprise datasets

Technical specifications

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