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Product Overview |
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The NVIDIA Tesla A10 24GB GDDR6 GPU Passive Cooler Computing Accelerator Card is a high-capacity graphics processing unit designed for intensive computational tasks. It fits into server and workstation environments where silent operation is essential, offering substantial memory and efficient processing power for AI and machine learning applications. |
General Information |
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| Brand | Nvidia |
| Part Number | 900-2G133-0120-130 |
| Series | NVIDIA A10 (Tesla A10) 24GB GDDR6 |
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 NVIDIA Tesla A10 24GB GPU accelerator is built to handle demanding compute tasks such as artificial intelligence, machine learning, and data analytics. Its passive cooling system is designed to work efficiently within server environments where airflow is managed externally, reducing noise and increasing reliability. Often found in data centers and research labs, this GPU card is favored by professionals and organizations that require robust performance for parallel processing and complex computations. It fits into PCIe slots, making it compatible with a wide range of computing systems. Key Features
This GPU accelerator is typically deployed in enterprise-grade servers to deliver accelerated computation without added noise. It plays a critical role in speeding up AI models and complex simulations while maintaining system stability in dense computing environments. |
Use Cases |
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The NVIDIA Tesla A10 24GB GDDR6 GPU Accelerator is primarily deployed in data centers and enterprise server environments where silent operation and high memory capacity are critical. It is designed for IT professionals and organizations focusing on AI-driven workloads, machine learning model training, and large-scale data processing within constrained noise environments. How It's Used:
By providing robust GPU acceleration with passive cooling, the Tesla A10 enables scalable performance enhancements without increasing noise or thermal disruption. This balance supports operational efficiency and resource optimization in mission-critical, AI-intensive deployments. |