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Product Overview |
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The Dell NVIDIA Tesla V100S 32GB HBM2 PCIe Passive GPU is designed to provide robust acceleration for computation-heavy applications in data centers and research labs. It fits into PCIe slots and uses passive cooling to maintain system efficiency in densely packed environments, delivering consistent performance for AI, deep learning, and HPC tasks. |
General Information |
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| Brand | Nvidia |
| Part Number | 900-2G500-0140-030 |
Technical Information |
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| Chipset | Nvidia |
| Bus Interface | PCIe 3.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, CUDA: 7.0, DirectCompute |
| Output Interface | None (no display outputs) |
Memory |
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| Memory Size | 32GB |
| Interface | HBM2 |
| Memory Bus | 4096-bit |
Physical Characteristics |
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| Slot Width | Dual Slot |
| Weight | 3.00 |
| Condition | Refurbished |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, CE, FCC |
Product Description |
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The Dell NVIDIA Tesla V100S is a high-performance GPU designed to accelerate demanding computational tasks. Commonly found in data centers and research facilities, it handles complex AI training, deep learning models, and scientific simulations with ease. Built for professionals and organizations requiring powerful parallel processing capabilities, this GPU supports environments where large-scale data throughput and efficiency are crucial. Key Features
This GPU is typically deployed in enterprise-grade servers and workstations running AI research, scientific computation, and large-scale simulations. It offers reliable performance that helps organizations push computational boundaries without compromising system stability or cooling requirements. |
Use Cases |
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The Dell NVIDIA Tesla V100S 32GB HBM2 PCIe Passive GPU is primarily utilized in high-density server environments where GPU acceleration is critical for large-scale computational workflows. It benefits data center operators and research institutions focusing on advanced AI models, scientific simulations, and other compute-intensive tasks requiring efficient thermal management in constrained spaces. How It's Used:
This GPU supports operational efficiency by delivering consistent computational performance while fitting into dense rack deployments with passive cooling. It enables scalable infrastructure designs where thermal constraints and high throughput must be balanced effectively. |