Overview

Power up your productivity, gaming, and content creation with the Intel Core i5-14600KF 3.5 GHz 14-Core LGA 1700 Processor. Built with the Intel 7 process, this 14th generation desktop processor delivers improved power efficiency while fitting the LGA 1700 socket. Featuring an optimized Hybrid Core Architecture, the Core i5-14600KF powers applications and games with six 3.5 GHz Performance-cores while the eight low-voltage Efficient-cores handle background tasks for smooth multitasking. 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 5.3 GHz Turbo Boost frequency, this processor is made to handle a variety of workloads. You can overclock this processor for even greater performance. The Core i5-14600KF also supports PCI Express 5.0 and up to 192GB of dual-channel DDR5 memory at 5600 MHz.

The Core i5-14600KF does not include integrated graphics, meaning that users will need to purchase a separate dedicated graphics card. Please note that a thermal solution is NOT included. This processor is backed by a 3-year warranty.

Hybrid Core Design

Performance-cores provide the speed to handle high-end games and demanding applications while low-priority and background tasks such as streaming video, playing music, and encoding media are handled by the processor’s Efficient-cores.

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.