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| Product Overview |
|---|
The IBM nVidia Tesla K10 combines two Kepler-based GPUs on a single accelerator card to tackle demanding compute tasks. It fits well into high-performance computing setups where parallel processing power is required for scientific calculations, simulations, and analytics. |
| General Information | |
|---|---|
| Brand | IBM |
| Part Number | 90Y2338 |
| Alternate Part Number | 90Y2338-03-CT |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| GPU Clock | 745 MHz |
| Bus Interface | PCI Express 3.0 x16 |
| Supported APIs | DirectX 11, OpenGL: 4.5, OpenCL, CUDA, Shader Model: 5.1, DirectCompute |
| Output Interface | None (compute accelerator, no display outputs) |
| Suggested PSU | 600W |
| Power Connectors | 2 x 6-pin PCIe |
| Memory | |
|---|---|
| Memory Size | 8GB |
| Interface | GDDR5 |
| Memory Bus | 256-bit (per GPU) |
| 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 IBM nVidia Tesla K10 is a powerful computing accelerator built for handling large-scale parallel workloads. It often finds its place in high-performance computing environments where rapid calculations and efficient processing are critical. Commonly used in research labs, data centers, and engineering firms, this card benefits users who rely on GPU acceleration to speed up simulations, data analysis, and rendering tasks. Key Features
These accelerators are typically deployed in clusters or workstations where massive computational throughput is necessary. They play a crucial role in accelerating workflows that would otherwise take much longer on traditional CPUs. |
| Use Cases |
|---|
The 90Y2338 IBM nVidia Tesla K10 accelerator is suited for various high-demand computing scenarios where GPU acceleration significantly enhances performance. How It's Used:
This accelerator is a key asset for professionals seeking to improve computational efficiency and throughput in demanding GPU-intensive tasks. |