EP2SGX130GF1508C4N

Applications

The EP2SGX130GF1508C4N is designed for high-performance computing environments, particularly in data centers and cloud computing services. It excels in applications requiring intensive computational tasks such as machine learning, big data processing, and scientific simulations.

In the financial sector, it supports complex algorithmic trading systems that require rapid data analysis and decision-making processes. Its robust performance ensures minimal latency, crucial for real-time market analysis.

For educational institutions, it powers advanced research projects involving artificial intelligence and deep learning models, enhancing the capabilities of academic research and development.

Operating Temperature: -20°C to +70°C

Key Advantages

1. High Clock Speed: The EP2SGX130GF1508C4N operates at up to 3.6 GHz, providing superior processing power compared to its predecessors.

2. Enhanced Memory Bandwidth: With a memory bandwidth of 256 GB/s, it significantly improves data transfer rates between the CPU and memory, optimizing overall system performance.

3. Power Efficiency: Equipped with a low-power consumption design, it reduces energy usage while maintaining high performance, making it ideal for environmentally conscious data centers.

4. Certification Standards: The chip adheres to stringent industry certifications, ensuring reliability and compliance with international standards.

Frequently Asked Questions

Q1: What is the maximum clock speed supported by the EP2SGX130GF1508C4N?

A1: The EP2SGX130GF1508C4N can operate at a maximum clock speed of 3.6 GHz.

Q2: Is the EP2SGX130GF1508C4N compatible with existing hardware?

A2: Yes, the EP2SGX130GF1508C4N is backward-compatible with most existing hardware configurations, but specific compatibility may vary depending on the motherboard and cooling solutions used.

Q3: In which specific scenarios would you recommend using the EP2SGX130GF1508C4N?

A3: The EP2SGX130GF1508C4N is recommended for scenarios requiring high computational power and low latency, such as real-time data analytics, high-frequency trading, and large-scale machine learning model training.

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