AMD Radeon Vega 2
AMD Radeon Vega 2 is a Integrated video accelerator from Intel. It began to be released in January 2020. The GPU has a boost frequency of Up to 1100 MHz. It also has a memory frequency of Up to DDR4-2400, platform dependent. Its characteristics, as well as benchmark results, are presented in more detail below.
Basic
Label Name
Intel
Platform
Integrated
Launch Date
January 2020
Former Codename
Dali / Raven Ridge
GPU Lithography
12 nm
Model Name
AMD Radeon Vega 2
Generation
Radeon Vega Mobile
Boost Clock
Up to 1100 MHz
Shading Units
?
The most fundamental processing unit is the Streaming Processor (SP), where specific instructions and tasks are executed. GPUs perform parallel computing, which means multiple SPs work simultaneously to process tasks.
128
RT Cores
No
Compute Units
2
Tensor Cores
?
Tensor Cores are specialized processing units designed specifically for deep learning, providing higher training and inference performance compared to FP32 training. They enable rapid computations in areas such as computer vision, natural language processing, speech recognition, text-to-speech conversion, and personalized recommendations. The two most notable applications of Tensor Cores are DLSS (Deep Learning Super Sampling) and AI Denoiser for noise reduction.
No
TMUs
?
Texture Mapping Units (TMUs) serve as components of the GPU, which are capable of rotating, scaling, and distorting binary images, and then placing them as textures onto any plane of a given 3D model. This process is called texture mapping.
8
Bus Interface
Integrated
Foundry
GlobalFoundries
Process Size
12 nm
Architecture
Vega
TDP
Shared with processor; typically 15 W APU TDP, 12-25 W configurable
Memory Specifications
Memory Size
Shared system memory
Memory Type
DDR4 shared system memory
Memory Bus
?
The memory bus width refers to the number of bits of data that the video memory can transfer within a single clock cycle. The larger the bus width, the greater the amount of data that can be transmitted instantaneously, making it one of the crucial parameters of video memory. The memory bandwidth is calculated as: Memory Bandwidth = Memory Frequency x Memory Bus Width / 8. Therefore, when the memory frequencies are similar, the memory bus width will determine the size of the memory bandwidth.
Dual-channel system memory, platform dependent
Memory Clock
Up to DDR4-2400, platform dependent
Bandwidth
?
Memory bandwidth refers to the data transfer rate between the graphics chip and the video memory. It is measured in bytes per second, and the formula to calculate it is: memory bandwidth = working frequency × memory bus width / 8 bits.
Up to 38.4 GB/s with dual-channel DDR4-2400
Display and Media
AMD FreeSync
Yes
AV1 Encode/Decode
No hardware support
H.264 Hardware Encode/Decode
Encode/Decode
H.265 HEVC Hardware Encode/Decode
Encode/Decode
H.266 VVC Hardware Encode/Decode
No hardware support
Intel Quick Sync Video
No
Outputs
HDMI, DisplayPort; device dependent
Theoretical Performance
Pixel Rate
?
Pixel fill rate refers to the number of pixels a graphics processing unit (GPU) can render per second, measured in MPixels/s (million pixels per second) or GPixels/s (billion pixels per second). It is the most commonly used metric to evaluate the pixel processing performance of a graphics card.
4.4 GPixel/s
Texture Rate
?
Texture fill rate refers to the number of texture map elements (texels) that a GPU can map to pixels in a single second.
8.8 GTexel/s
FP16 (half)
?
An important metric for measuring GPU performance is floating-point computing capability. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy.
0.56 TFLOPS
FP64 (double)
?
An important metric for measuring GPU performance is floating-point computing capability. Double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy, while single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.
17.6 GFLOPS
FP32 (float)
?
An important metric for measuring GPU performance is floating-point computing capability. Single-precision floating-point numbers (32-bit) are used for common multimedia and graphics processing tasks, while double-precision floating-point numbers (64-bit) are required for scientific computing that demands a wide numeric range and high accuracy. Half-precision floating-point numbers (16-bit) are used for applications like machine learning, where lower precision is acceptable.
0.28
TFlops
AI Features
Intel Deep Learning Boost on GPU
No
Miscellaneous
PCI Express Version
PCIe 3.0
Vulkan Version
?
Vulkan is a cross-platform graphics and compute API by Khronos Group, offering high performance and low CPU overhead. It lets developers control the GPU directly, reduces rendering overhead, and supports multi-threading and multi-core processors.
1.2
OpenCL Version
1.2
OpenGL
4.6
CUDA
No
DirectX
12 (12_1)
Power Connectors
None
ROPs
?
The Raster Operations Pipeline (ROPs) is primarily responsible for handling lighting and reflection calculations in games, as well as managing effects like anti-aliasing (AA), high resolution, smoke, and fire. The more demanding the anti-aliasing and lighting effects in a game, the higher the performance requirements for the ROPs; otherwise, it may result in a sharp drop in frame rate.
4
FP32 (float)
0.28
TFlops
Compared to Other GPU
SiliconCat Rating
1148
Ranks 1148 among all GPU on our website
FP32 (float)
