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| Product Overview |
|---|
The Lenovo Intel Xeon Phi 5110P accelerator card integrates a 60-core processor designed specifically to boost parallel processing performance. It fits into servers that require enhanced compute capabilities for scientific and analytic applications. This card handles workloads that demand extensive multi-threading, making it a valuable addition in environments focused on high-performance computing. |
| General Information | |
|---|---|
| Brand | Lenovo |
| Part Number | 90Y2367 |
| Series | Intel Xeon Phi 5110P |
| Miscellaneous | |
|---|---|
| Eco Friendly | Yes |
| Compliance Standards | WEEE, RoHS, CE, FCC |
| Physical Characteristics | |
|---|---|
| Weight | 3.00 |
| Condition | Refurbished |
| Product Description |
|---|
This Lenovo accelerator card houses the Intel Xeon Phi 5110P processor, built for massively parallel processing tasks. It is often found in data centers and research environments requiring extensive compute power beyond traditional CPUs. Commonly used in high-performance computing clusters, this card helps speed up workloads such as simulations, machine learning, and large-scale data analysis. Organizations involved in scientific research, engineering, or analytics benefit from integrating this into their server setups. Key Features
In practice, this card is deployed in Lenovo servers used for research simulations, financial modeling, and AI training. It allows data centers to handle complex calculations more efficiently by boosting parallel processing capabilities. Its role in clusters enables users to tackle problems that require splitting tasks across many cores, cutting down processing time and improving throughput. |
| Use Cases |
|---|
The 90Y2367 Lenovo Intel Xeon Phi 5110P accelerator card is utilized across various industries requiring accelerated computation and parallel processing capabilities. How It's Used:
This accelerator card enables organizations to scale their computational tasks efficiently, reducing processing times and improving overall system performance in data-intensive applications. |