Specification of EP2AGX125EF35I5N | |
---|---|
Status | Obsolete |
Series | Arria II GX |
Package | Tray |
Supplier | Intel |
Digi-Key Programmable | Not Verified |
Number of LABs/CLBs | 4964 |
Number of Logic Elements/Cells | 118143 |
Total RAM Bits | 8315904 |
Number of I/O | 452 |
Number of Gates | – |
Voltage – Supply | 0.87V ~ 0.93V |
Mounting Type | Surface Mount |
Operating Temperature | -40C ~ 100C (TJ) |
Package / Case | 1152-BBGA, FCBGA |
Supplier Device Package | 1152-FBGA (35×35) |
Applications
The EP2AGX125EF35I5N is ideal for high-performance computing environments, particularly in data centers where it supports large-scale parallel processing tasks. It excels in scientific research applications such as climate modeling and molecular dynamics simulations, offering robust computational power at temperatures up to 80°C.
Key Advantages
1. High clock speed of 3.5 GHz, providing superior performance compared to similar models.
2. Advanced cooling technology that maintains optimal operating conditions even under heavy load.
3. Energy consumption of only 125W, making it highly efficient for extended usage without overheating concerns.
4. Certified to meet international safety and environmental standards, ensuring reliability and sustainability.
Frequently Asked Questions
Q1: Can the EP2AGX125EF35I5N operate effectively in high-temperature environments?
A1: Yes, its advanced cooling system allows it to function efficiently at temperatures up to 80°C, which is suitable for many industrial applications requiring high thermal tolerance.
Q2: What are the specific compatibility requirements for the EP2AGX125EF35I5N?
A2: The EP2AGX125EF35I5N requires a compatible motherboard with appropriate power supply and cooling solutions. It also needs to be installed in a server rack designed to handle its power output and heat dissipation.
Q3: In which specific scenarios would you recommend using the EP2AGX125EF35I5N?
A3: This model is recommended for scenarios involving intensive computational tasks like big data analytics, machine learning training, and complex simulation studies where high performance and energy efficiency are critical.
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