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Product Overview |
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The Dell NVIDIA Tesla A10 24GB GDDR6 video graphics card offers powerful GPU acceleration tailored for AI and computational tasks. It fits well within enterprise servers and data center environments that demand robust processing capabilities without compromising reliability or scalability. |
General Information |
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| Brand | Dell |
| Part Number | 6KNWW |
Technical Information |
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| Chipset | Nvidia |
Memory |
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| Memory Size | 24GB |
| Interface | GDDR6 |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
Physical Characteristics |
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| Slot Width | Dual-slot |
| Weight | 3.00 |
| Condition | Refurbished |
Product Description |
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The Dell NVIDIA Tesla A10 is built for high-performance computing workloads, including AI training, inference, and complex data analytics. It's commonly found in data centers and workstations where demanding GPU acceleration is required. Professionals in fields like AI research, scientific computing, and advanced visualization often utilize this card to speed up their workflows. Key Features
This graphics card is typically deployed in servers and specialized workstations where intensive processing is routine. It helps reduce processing times for complex algorithms and enables faster iteration cycles, which is crucial in research and enterprise environments. |
Use Cases |
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The Dell NVIDIA Tesla A10 24GB GPU accelerator is predominantly deployed in data center and enterprise server environments where high-performance computing is critical. It is tailored for IT professionals and organizations focused on AI workloads, machine learning training, and complex visualization tasks requiring consistent and scalable GPU resources. How It's Used:
By integrating with multi-GPU configurations, this accelerator helps organizations optimize workload throughput while maintaining operational scalability. Its design supports sustained performance in demanding server environments, facilitating efficient resource utilization across evolving computational demands. |