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Product Overview |
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The HPE NVIDIA Tesla M6 8GB GPU video card offers efficient GPU acceleration tailored for virtualized server environments. It's designed to fit into high-density setups and supports multiple users simultaneously, making it a practical choice for businesses running virtual desktop infrastructure. |
General Information |
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| Brand | HPE |
| Part Number | 805132-B21 |
Technical Information |
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| Chipset | Nvidia |
| Bus Interface | PCIe 3.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.5, OpenCL, CUDA, Shader Model: 5.1, DirectCompute |
| Output Interface | None |
| Power Connectors | None |
Memory |
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| Memory Size | 8GB |
| Interface | GDDR5 |
Physical Characteristics |
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| 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 HPE NVIDIA Tesla M6 8GB GPU video card is built for virtual desktop infrastructure (VDI) and data center workloads that require reliable GPU acceleration. It’s commonly used in enterprise servers to enable smooth graphical performance across multiple users. Often found in business environments needing robust graphics support without sacrificing space, this GPU caters to IT professionals managing virtualized applications or remote workstations. Key Features
This GPU card is typically deployed in corporate data centers or cloud environments where resource sharing and graphical performance are critical. It plays a key role in improving user experience for virtualized desktops and remote applications without the need for individual physical GPUs. |
Use Cases |
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The HPE NVIDIA Tesla M6 8GB GPU is primarily employed in virtualized data center environments where multiple virtual desktops or applications require efficient GPU acceleration. It is especially suited for enterprise IT infrastructures that demand high-density server deployments with optimized resource sharing among users. How It's Used:
This GPU card enhances operational efficiency by consolidating graphical workloads within virtualized servers, reducing hardware redundancy. Its design for dense server environments aids scalability, allowing IT teams to expand GPU capacity without extensive infrastructure changes. |