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Intel - Core i5-13600KF 13th - LGA1700 Unlocked Desktop Processor
Intel - Core i5-13600KF 13th - LGA1700 Unlocked Desktop Processor
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Price:FL 1,085.00
As low as FL 131.10/month with
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Description:
Power up your productivity, gaming, and content creation with
the Intel Core i5-13600KF 3.5 GHz 14-Core LGA 1700 Processor. This
13th generation desktop processor builds upon the Hybrid Core architecture and
Intel 7 process by fitting 14 cores— four more than the previous generation—
onto a single processor while still supporting the LGA 1700 socket. Designed to
provide strong performance for everyday tasks, the Core i5-13600KF features
eight low-voltage Efficient-cores that handle background tasks for multitasking
while the six 3.5 GHz Performance-cores power applications and games. The
built-in Intel Thread Director ensures that the two work seamlessly together by
dynamically and intelligently assigning workloads to the right core at the
right time.
With 24MB of cache and a
5.1 GHz Turbo Boost Max 2.0 frequency, this processor is made to handle a
variety of applications. You can also overclock this processor for even greater
performance. The Core i5-13600KF also supports PCI Express 5.0 and dual-channel
DDR5 memory at 5600 MHz.
Hybrid Core Design
Performance-cores provide the speed to handle high-end games and
demanding applications while low-priority and background tasks are handled by
the processor's Efficient-cores for multitasking.
Intel Thread Director
The Intel Thread Director is built into the CPU's cores, working
with the operating system to ensure each of the 20 threads are assigned to the
right core at the right time.
PCIe 4.0 & 5.0
This processor supports up to four PCIe 4.0 and sixteen PCIe 5.0
lanes, delivering 20 lanes in total for exceptional data throughput with
compatible devices.
Gaussian & Neural Accelerator 3.0
Gaussian and Neural Accelerator 3.0 (GNA) technology helps with
noise suppression while enhancing background blurring during video chats.
Intel Deep Learning Boost
Accelerates AI inference to improve performance for deep learning
workloads.
