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| Product Overview |
|---|
The IBM Nvidia Tesla K20 GK110 5GB GDDR5 PCI-Express Video Graphics Card delivers specialized GPU acceleration mainly for compute-intensive applications. It fits into professional setups requiring enhanced parallel processing capabilities, such as scientific calculations and data analysis, rather than standard graphics rendering. |
| General Information | |
|---|---|
| Brand | IBM |
| Part Number | 00FP673 |
| Alternate Part Number | 00FP673-03-CT |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| GPU Clock | 706 MHz |
| Bus Interface | PCI Express 3.0 x16 |
| Supported APIs | OpenCL, CUDA |
| Output Interface | None (compute card, no display outputs) |
| Suggested PSU | 600W |
| Power Connectors | 6-pin,8-pin |
| Memory | |
|---|---|
| Memory Size | 5GB |
| Interface | GDDR5 |
| Memory Bus | 320-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 IBM Nvidia Tesla K20 is a powerful graphics card built for demanding computational workloads rather than gaming. It is often found in scientific research, engineering simulations, and deep learning environments where high parallel processing power is essential. Commonly used in data centers and professional workstations, this card helps accelerate complex calculations by offloading them from the CPU. Researchers, engineers, and developers benefit from its capability to handle large datasets and intensive numerical tasks efficiently. Key Features
This graphics card is typically deployed in environments focused on high-performance computing, such as research labs and enterprise-grade servers. It plays a critical role in reducing computation times and enabling more complex simulations or machine learning models. |
| Use Cases |
|---|
The 00FP673 IBM Nvidia Tesla K20 card is well-suited for environments requiring substantial parallel computing power. It is commonly deployed in scientific research, engineering, and data analytics. How It's Used:
These use cases highlight the card's role in advancing computational efficiency and enabling complex analyses across various technical fields. |