The Core i9-13900KS features integrated Intel UHD 770 Graphics. 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 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 32 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.
Thermal Velocity Boost
Intel Thermal Velocity Boost (TVB) is a technology that unlocks extra CPU performance by pushing the maximum frequency to 6 GHz when thermal headroom and turbo power budget are available. It is ideal for burst workloads that have sudden spikes in CPU utilization, providing a temporary performance boost to help avoid slowdowns.
Adaptive Boost
Intel Adaptive Boost Technology opportunistically increases the all-core turbo frequency beyond previous Turbo Boost clock speeds, even when Thermal Velocity Boost conditions aren’t met. Benefits can be seen with multi-threaded programs that scale with multiple cores, which includes many games. It can also help in multitasking situations, such as when gaming, streaming, and chatting all at once through different applications.
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.
