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Product Overview |
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The Lenovo NVIDIA Tesla A2 16GB GPU professional computing card provides focused GPU acceleration primarily for AI inference and machine learning tasks. It fits well into compact server systems and is crafted to complement Lenovo’s infrastructure, delivering solid performance for specialized workloads without excessive power or space demands. |
General Information |
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| Brand | Lenovo |
| Part Number | 699-2G179-0220-001 |
Technical Information |
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| Chipset | Nvidia |
| Bus Interface | PCI Express 4.0 x16 |
| Supported APIs | OpenGL: 4.6, OpenCL, Vulkan: 1.2.175 , CUDA, DirectCompute |
| Output Interface | No display outputs (compute accelerator) |
| 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 |
| Weight | 3.00 |
| Condition | Refurbished |
Miscellaneous |
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| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, CE, FCC |
Product Description |
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The Lenovo NVIDIA Tesla A2 16GB GPU is built for demanding professional computing tasks such as AI model training, inference, and data analytics. This powerful card leverages NVIDIA's Ampere architecture to deliver efficient acceleration for machine learning workflows and scientific simulations. Often found in data centers and research labs, the Tesla A2 is designed to work seamlessly within Lenovo servers and workstations. It's a popular choice for professionals who need reliable GPU performance in a compact, energy-efficient package. Key Features
In practice, the Tesla A2 is frequently deployed in AI research environments and enterprise edge computing setups. It helps organizations accelerate complex calculations without requiring large-scale GPU installations, making it an accessible option for diverse professional uses. |
Use Cases |
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The Lenovo NVIDIA Tesla A2 16GB GPU is commonly deployed in compact server environments where AI inference and machine learning workloads demand efficient, space-conscious acceleration. It suits IT teams managing edge computing nodes or data centers requiring targeted GPU resources without the footprint of larger GPUs. How It's Used:
This GPU enables organizations to scale AI inference capabilities efficiently while maintaining manageable power and space requirements. Its compatibility with compact server architectures helps IT managers balance performance demands against infrastructure constraints in specialized computing scenarios. |