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| Product Overview |
|---|
The HP Tesla A40 48GB GDDR6 PCIe 4.0 Video Graphics Card is a high-performance computing component designed to tackle resource-intensive tasks. It fits within the Apollo 6500 Gen10 Plus system and provides ample memory and fast data transfer capabilities, making it suitable for AI training, simulations, and other demanding applications that require both speed and capacity. |
| General Information | |
|---|---|
| Brand | HP |
| Part Number | P38998-001 |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| Bus Interface | PCIe 4.0 x16 |
| Supported APIs | DirectX 12, OpenGL: 4.6, OpenCL, Vulkan: 1.2.175 , CUDA, Shader Model: 6.7, DirectCompute |
| Suggested PSU | System dependent — follow HP Apollo 6500 Gen10 Plus power provisioning. For single-GPU workstation use, typical recommended PSU ≥750W; for multi‑GPU Apollo configurations use HP-specified power supplies per system configuration |
| Memory | |
|---|---|
| Memory Size | 48GB |
| Interface | GDDR6 |
| Memory Bus | 384-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Dual-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 HP Tesla A40 graphics card is built for demanding computational workloads that require high memory and fast processing. Its 48GB of GDDR6 memory allows users to manage extensive datasets and run complex models without memory bottlenecks. Commonly used in data centers and research facilities, this card fits seamlessly into the Apollo 6500 Gen10 Plus platform. Often found in AI training environments and scientific research setups, the Tesla A40 offers a balance of speed and memory capacity that benefits data scientists, engineers, and researchers. Its PCIe 4.0 interface provides enhanced bandwidth, improving the efficiency of data-intensive tasks. Key Features
This graphics card is typically deployed in enterprise data centers where complex simulations and AI model training are routine. It helps accelerate workflows that involve large-scale data processing, reducing wait times and improving overall system performance. Users will find it especially valuable in environments where computational power and memory capacity are critical for success. |