Intel Core i7 14700K / 1700 Tray




3 שנים


Intel® Core™ i7 processor 14700K C14700KT 33M Cache, up to 5.60 GHz 1700 C14700KT Tray


סדרת מעבד (CPU)

Intel Core i7 

דור המעבד

דור 14 

תושבת מעבד


כמות ליבות מעבד

20 ליבות 

מעבד גרפי

Intel UHD 770 

תאימות ערכת שבבים

Intel Z690 
\ Intel H610 
\ Intel B660 
\ Intel Z790 
\ Intel B760 
\ Intel H770 
\ Intel Q670 
\ Intel H670 

Threads #


מהירות מעבד

Base 2.5 GHz | Max Turbo 5.6 GHz 

זכרון מטמון במעבד

L1 Cache 33 MB | L2 Cache 28 MB 

צריכת חשמל - מעבד

Base 125W | Maximum Turbo 253 W 


Tray - מגש ללא אריזה 


Key Features

  • 20 Cores & 28 Threads
  • 3.4 GHz P-Core Clock Speed
  • 5.6 GHz Maximum Turbo Boost Frequency
  • LGA 1700 Socket
  • 30MB Cache Memory
  • Dual-Channel DDR5-5600 ECC Memory
  • Integrated Intel UHD 770 Graphics
  • Hybrid Core Architecture
  • Unlocked for Overclocking
  • 14th Generation (Raptor Lake-S Refresh)

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 28 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.

Integrated Graphics

Driven by Intel Xe architecture, the integrated Intel UHD 770 Graphics delivers fast, rich 3D performance for high-quality visuals with support for either one 7680 x 4320 resolution 8K monitor or up to four 3840 x 2160 resolution 4K displays with high-dynamic range.

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.