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| Product Overview |
|---|
The Lenovo Intel Xeon Phi 7120A accelerator card is a specialized computational tool designed to add parallel processing power to compatible servers. It fits into systems where handling many simultaneous threads is crucial, helping to accelerate workloads that demand high core counts and memory bandwidth without replacing the main processor. |
| General Information | |
|---|---|
| Brand | Lenovo |
| Part Number | 00MU958 |
| Series | Intel Xeon Phi 7120A |
| Miscellaneous | |
|---|---|
| Eco Friendly | Yes |
| Compliance Standards | RoHS, CE |
| Physical Characteristics | |
|---|---|
| Weight | 3.00 |
| Condition | Refurbished |
| Product Description |
|---|
This Lenovo accelerator card is built around the Intel Xeon Phi 7120A processor, designed to boost performance in highly parallel computing tasks. It fits into servers to speed up workloads that benefit from many cores working simultaneously, such as scientific simulations, data analytics, and machine learning. Often found in high-performance computing clusters and research labs, this card suits users needing substantial computational power beyond standard CPUs. Engineers, researchers, and data scientists can leverage its parallel architecture to handle complex calculations more efficiently. Key Features
This card is typically deployed in environments that require large-scale numerical processing, such as universities, research centers, and enterprises running simulations or deep learning tasks. Its parallel design helps reduce processing time on complex problems by distributing work across many cores. |
| Use Cases |
|---|
The 00MU958 Lenovo Intel Xeon Phi 7120A accelerator card is designed for high-performance computing environments where parallel processing with many cores is essential. It is ideal for data centers and research institutions that require acceleration for compute-intensive applications such as simulations, analytics, and complex modeling. How It's Used:
This accelerator card delivers scalable performance improvements by offloading parallel workloads from the main CPU, enabling more efficient use of server resources. Its integration supports operational scalability and throughput in environments demanding high core counts and memory bandwidth. |