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| Product Overview |
|---|
The Cisco NVIDIA Tesla A40 48GB GPU is designed as a powerful accelerator for AI, machine learning, and advanced graphics workloads. It fits into high-performance computing setups where handling large datasets and complex computations is critical. Its combination of large memory and NVIDIA's Ampere architecture makes it a solid choice for professionals who need reliable GPU performance without compromise. |
| General Information | |
|---|---|
| Brand | Cisco |
| Part Number | 900-2G133-6200-030 |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCI Express 4.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, OpenCL: 3.0, Vulkan: 1.2.175 , CUDA, Shader Model: 6.7, DirectCompute |
| Output Interface | None (compute card, no display outputs) |
| Memory | |
|---|---|
| Memory Size | 48GB |
| Interface | GDDR6 |
| Memory Bus | 384-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | 2-slot |
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, CE, FCC, UL, TUV |
| Product Description |
|---|
The Cisco NVIDIA Tesla A40 48GB GPU is built for heavy computational tasks like deep learning, AI training, and scientific simulations. It's commonly found in data centers and research facilities where large-scale data processing is essential. This graphics card is often used by researchers, data scientists, and engineers who need robust GPU acceleration to speed up their workflows. Its large memory capacity helps handle complex models and datasets without bottlenecks. Key Features
You'll typically see this GPU deployed in enterprise AI servers, high-performance computing clusters, and workstations focused on graphics-intensive tasks. It provides dependable power for complex computations without frequent memory limitations. |
| Use Cases |
|---|
The Cisco NVIDIA Tesla A40 48GB GPU is typically deployed in enterprise data centers and research institutions that demand intensive computational power for AI and deep learning tasks. It benefits environments where large-scale machine learning models and complex data analytics workflows require substantial GPU resources and high memory capacity. How It's Used:
This graphics card supports operational efficiency by enabling scalable GPU compute capabilities within rack-mounted servers, ensuring robust performance for demanding AI and visualization applications. Its integration into high-density computing environments helps maintain consistent throughput while accommodating evolving workload complexities. |