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Product Overview |
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The NVIDIA Tesla A2 16GB GPU is a professional computing card tailored for efficient acceleration in compact systems. It fits well in environments needing reliable AI and data processing power without large hardware footprints. |
General Information |
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| Brand | Nvidia |
| Part Number | 699-2G179-0220-200 |
Technical Information |
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| Chipset | Nvidia |
| Bus Interface | PCIe 4.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, OpenCL: 3.0, Vulkan: 1.2.175 , Vulkan: 1.3, CUDA, DirectCompute |
| Output Interface | None (compute card, no display outputs) |
| Power Connectors | None |
Memory |
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| Memory Size | 16GB |
| Interface | GDDR6 |
| Memory Bus | 128-bit |
Physical Characteristics |
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| Slot Width | Single-slot, Low-profile |
| 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 NVIDIA Tesla A2 16GB GPU is built for professional computing environments where space and efficiency matter. It is commonly used in edge servers and compact workstations that require reliable acceleration for AI and data workloads. Designed for professionals in fields like machine learning, data analysis, and visualization, this GPU handles demanding tasks without occupying much physical space. Key Features
You'll find the Tesla A2 16GB in setups where compact size and computing power need to come together, like remote workstations or edge data centers. Its balance of efficiency and performance helps streamline workloads that benefit from GPU acceleration. |
Use Cases |
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The NVIDIA Tesla A2 16GB GPU is designed for deployment in compact workstation and edge server environments where space and power efficiency are critical. It is ideally suited for organizations requiring reliable AI inference and data processing capabilities without the overhead of large-scale GPU infrastructure. How It's Used:
By integrating the Tesla A2, organizations can maintain operational efficiency in constrained physical environments while scaling GPU compute performance to meet diverse workload demands. This balance supports consistent performance without necessitating large, power-intensive systems. |