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Product Overview |
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The HPE NVIDIA Tesla V100 PCIe 32GB Computational Accelerator delivers powerful GPU resources within a PCIe slot, making it a versatile option for boosting computational performance. It fits well in servers focused on AI development, deep learning projects, and data-intensive simulations, helping professionals get their work done faster and more efficiently. |
General Information |
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| Brand | HPE |
| Part Number | P05470-001 |
| Series | NVIDIA Tesla V100 PCIe 32GB |
Miscellaneous |
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| Compliance Standards | WEEE, RoHS, CE, FCC |
Physical Characteristics |
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| Weight | 3.00 |
| Condition | Refurbished |
Product Description |
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The HPE NVIDIA Tesla V100 PCIe 32GB Computational Accelerator is built for heavy-duty parallel processing and AI applications. It's commonly found in data centers where AI training, machine learning, and high-performance computing are critical. Engineers, researchers, and data scientists rely on this accelerator to speed up complex calculations and model training. Key Features
This accelerator is often deployed in high-performance computing clusters and AI workstations where rapid data processing is essential. It helps reduce training times for complex neural networks and accelerates scientific computing, making it a valuable tool in research labs and enterprise environments. |
Use Cases |
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The HPE NVIDIA Tesla V100 PCIe 32GB Computational Accelerator is commonly deployed in high-performance computing clusters and enterprise data centers requiring substantial GPU compute power. It is particularly suited to AI researchers, data scientists, and engineers working on machine learning models or large-scale simulations in virtualized rack-based environments. How It's Used:
This accelerator facilitates scalable processing capabilities and improved compute density, helping organizations optimize resource utilization across demanding workloads. Its PCIe form factor allows integration into standard server infrastructures, supporting operational efficiency and consistent performance growth. |