
Subscribe To Our Newsletter!
Subscribe to the newsletter to stay up to date with the latest news and most useful
Newsletter
↑
Back to Top
| Product Overview |
|---|
The Lenovo NVIDIA Tesla A2 16GB GPU card delivers focused GPU acceleration for professional workloads in compact and virtualized environments. It fits well in data centers and servers that require efficient power use and robust performance for AI and HPC tasks without excessive hardware footprint. |
| General Information | |
|---|---|
| Brand | Lenovo |
| Part Number | 900-2G179-2720-001 |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCI Express 4.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, Vulkan: 1.3, CUDA, DirectCompute |
| Output Interface | None (compute card, no display outputs) |
| Power Connectors | None (powered via PCIe slot) |
| Memory | |
|---|---|
| Memory Size | 16GB |
| Interface | GDDR6 |
| Memory Bus | 128-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Single-slot |
| Weight | 3.00 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, cURus, CE, FCC, CCC, UL, TUV, cULus, CSA, cUL |
| Product Description |
|---|
The Lenovo NVIDIA Tesla A2 16GB GPU card is designed to accelerate a wide range of professional computing tasks. It is often found in data centers and workstations where machine learning, AI inference, and virtualization performance are critical. Built for IT professionals and researchers, this GPU card integrates seamlessly into servers and high-performance computing setups to enhance efficiency and throughput. It's commonly used in environments that demand reliable and scalable GPU acceleration. Key Features
This GPU card is typically deployed in server farms, AI labs, and virtualized computing environments. It helps professionals run complex computations faster without requiring excessive power or space. Its role in accelerating AI inference and virtualization makes it a practical choice for organizations aiming to maximize GPU resources efficiently. |