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Product Overview |
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The Cisco NVIDIA Tesla A40 48GB is a powerful GPU designed for deep learning and AI workloads in professional and enterprise settings. It fits seamlessly into server environments, providing the large memory and robust architecture needed to accelerate demanding computational tasks in data science and machine learning. |
General Information |
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| Brand | Cisco |
| Part Number | 30-100281-01 |
Technical Information |
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| Chipset | Nvidia |
| Bus Interface | PCI Express 4.0 x16 |
| Supported APIs | DirectX 11, DirectX 12, OpenGL: 4.6, OpenCL, Vulkan: 1.2.175 , CUDA, Shader Model: 6.7, DirectCompute |
| Output Interface | None (compute/server GPU - no display outputs) |
Memory |
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| Memory Size | 48GB |
| Interface | GDDR6 |
| Memory Bus | 384-bit |
Physical Characteristics |
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| Slot Width | 2-slot |
| Weight | 3.00 |
| Condition | Refurbished |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
Product Description |
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The Cisco NVIDIA Tesla A40 48GB GPU is built for intensive deep learning and AI applications that demand high memory capacity and computational power. It is often found in data centers and research labs where large neural networks are trained and complex simulations are run. This graphics card is commonly used by AI developers, data scientists, and researchers who need reliable acceleration for their machine learning pipelines. Key Features
Typically deployed in enterprise servers or AI research clusters, this GPU facilitates faster training times and more complex model development. Users benefit from its ability to handle detailed computations without bottlenecks, making it an essential component in professional AI environments. |
Use Cases |
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The Cisco NVIDIA Tesla A40 48GB GPU is primarily deployed in data centers and enterprise server environments where large-scale AI and deep learning tasks demand significant computational resources and memory capacity. It benefits AI researchers, data scientists, and IT professionals managing intensive machine learning workflows that require accelerated data processing and model training. How It's Used:
This GPU facilitates operational efficiency by enabling high-performance processing within dense server environments, supporting scalability through its large memory and robust architecture. Such capabilities allow organizations to handle complex AI workloads with improved throughput and reduced time to insight. |