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| Product Overview |
|---|
The IBM Nvidia TESLA K20 GPU Computing Accelerator delivers powerful parallel processing capabilities designed specifically for computational tasks in high-performance environments. It fits well in data centers and professional computing setups where accelerating workloads is essential. |
| General Information | |
|---|---|
| Brand | Nvidia |
| Part Number | 90Y2391 |
| Alternate Part Number | 90Y2391-03-CT |
| Technical Information | |
|---|---|
| Chipset | Nvidia |
| GPU Clock | 706 MHz |
| Bus Interface | PCI Express 3.0 x16 |
| Supported APIs | OpenCL, CUDA, DirectCompute |
| Output Interface | None (compute card, no display outputs) |
| Suggested PSU | 600W |
| Power Connectors | 1 x 6-pin PCIe |
| Memory | |
|---|---|
| Memory Size | 5GB |
| Interface | GDDR5 |
| Memory Bus | 320-bit |
| Physical Characteristics | |
|---|---|
| Slot Width | Dual-slot |
| Weight | 1.80 |
| Condition | Refurbished |
| Miscellaneous | |
|---|---|
| Assembly Required | Yes |
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, CE, FCC, UL, TUV |
| Product Description |
|---|
The IBM Nvidia TESLA K20 GPU Computing Accelerator is a specialized graphics processing unit tailored for demanding computational workloads. It's often found in data centers and research facilities where heavy parallel processing is required. Built for professionals working in scientific computing, engineering simulations, and machine learning, this GPU helps speed up calculations that would otherwise take much longer on traditional CPUs. Key Features
This accelerator is commonly deployed in HPC clusters and enterprise servers to boost data throughput and reduce computational time. It plays a key role in research labs and companies that require fast processing of complex models and simulations. |
| Use Cases |
|---|
The 90Y2391 IBM Nvidia TESLA K20 GPU Computing Accelerator is utilized in environments requiring accelerated computational performance. How It's Used:
Its integration into IBM systems allows organizations to efficiently handle large-scale computations and reduce time-to-insight for complex data-driven projects. |