AMD Radeon RX 6000 Graphics Cards

AMD Radeon RX 6000 on Amazon USA

Specifications of the video cards present in the group of graphics cards AMD Radeon RX 6000. We also have a comparison of the respective performances with the benchmarks, the power in terms of GFLOPS FP16, GFLOPS FP32, GFLOPS FP64 if available, the filling rate in GPixels/s, the filtering rate in GTexels/s.


AMD Radeon RX 6950 XT 2022 Q2 7 nm 1925 2324 128000 16000 335 5120 80 320 128 80 297.5 743.7 47600 23800 1487
AMD Radeon RX 6900 XT 2020 Q4 7 nm 1825 2250 128000 16000 300 110 5120 80 320 128 80 288 720 46080 23040 1440
AMD Radeon RX 6800 XT 2020 Q4 7 nm 1825 2250 128000 16000 300 110 4608 72 288 128 72 288 648 41470 20740 1296
AMD Radeon RX 6800 2020 Q4 7 nm 1700 2105 128000 16000 250 110 3840 60 240 96 60 202.1 505.2 32333 16166 1010.4
AMD Radeon RX 6750 XT 2022 Q1 7 nm 2150 2600 96000 12000 250 2560 40 160 64 40 166.4 416 26620 13310 832
AMD Radeon RX 6700 XT 2021 Q1 7 nm 2321 2581 96000 12000 230 110 2560 40 160 64 40 165.2 413 26429 13215 825.9
AMD Radeon RX 6700 2021 Q2 7 nm 1941 2450 80000 10000 175 110 2304 36 144 64 36 156.8 352.8 22579 11290 705.6
AMD Radeon RX 6650 XT 2022 Q2 7 nm 2055 2635 32000 8000 176 2048 32 128 64 32 168.6 337.3 21586 10793 674.6
AMD Radeon RX 6600 XT 2021 Q3 7 nm 1968 2589 32000 8000 160 110 2048 32 128 64 32 165.7 331.4 21210 10600 662.8
AMD Radeon RX 6600 2021 Q3 7 nm 2200 2500 32000 8000 150 110 1792 28 112 64 28 160 280 17920 8960 560
AMD Radeon RX 6500 XT 2022 Q1 6 nm 2310 2815 16000 4000 107 1024 16 64 32 16 90.08 180.2 11530 5765 360.3
AMD Radeon RX 6400 2022 Q3 6 nm 1800 2000 16000 4000 100 768 12 48 32 12 64 96 6144 3072 192

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TMUs: texture mapping units. This unit was a physical processor separated from the main graphics processing units. It is able to distort a bitmap image, and also to resize it, to rotate it and to place it as a texture onto a plane of a specific 3D model.

ROPs: render output units. The other name of this unit is raster operations pipeline. This hadware component is taking pixel and texel information, and processing it through vector and matrix operations. The result will be a depth value or a final pixel. It is able to control antialiasing with the merge of several sample into one pixel.

SM / SMX, CUs (compute units) : streaming multiprocessor. These processors are implied in the Kepler's power efficiency when the GPU is using one unified clock speed.

The term pixel fillrate is refering to the number of pixels that the video card is able to generate every second. This performance is achieved by multiplying the raster output units (ROPs) by the clock frequency of the graphics processor unit (GPU). Matrix and vector operations allow pixel pipelines to process pixels and texels stored in the buffer. The ROPs will then generate transactions between the buffer and local memory. You should know that the higher the pixel rate, the greater the screen resolution that the GPU can manage.

The term texture fillrate refers to the number of map texture elements (texels) that the GPU is capable of generating per second. This performance is achieved by multiplying the texture mapping units (TMUs) by the clock frequency of the graphics processor unit. To make a 3D scene in a video game or in 3D editing software such as Blender, the graphics card must perform what is called texture mapping with the texture mapping units.

Performance with the benchmarks:

FP16 performance comparison (half precision; float; floating-point 16) for this graphics card:

FP16 Performance in GFLOPS
AMD Radeon RX 6950 XT 47,600
AMD Radeon RX 6900 XT 46,080
AMD Radeon RX 6800 XT 41,470
AMD Radeon RX 6800 32,333
AMD Radeon RX 6750 XT 26,620
AMD Radeon RX 6700 XT 26,429
AMD Radeon RX 6700 22,579
AMD Radeon RX 6650 XT 21,586
AMD Radeon RX 6600 XT 21,210
AMD Radeon RX 6600 17,920
AMD Radeon RX 6500 XT 11,530
AMD Radeon RX 6400 6,144

The power in FP16, i.e. in a half-precision floating point format, makes it possible to evaluate the ability of the graphics card to manipulate useful data for example in computer applications, such as deep learning with neural networks. These applications use numeric values that have less precision, and therefore require less memory, compared to values with single or double precision formats.

FP32 performance comparison (single precision; float; floating-point 32) for this graphics card:

FP32 Performance in GFLOPS
AMD Radeon RX 6950 XT 23,800
AMD Radeon RX 6900 XT 23,040
AMD Radeon RX 6800 XT 20,740
AMD Radeon RX 6800 16,166
AMD Radeon RX 6750 XT 13,310
AMD Radeon RX 6700 XT 13,215
AMD Radeon RX 6700 11,290
AMD Radeon RX 6650 XT 10,793
AMD Radeon RX 6600 XT 10,600
AMD Radeon RX 6600 8,960
AMD Radeon RX 6500 XT 5,765
AMD Radeon RX 6400 3,072

Single-precision floating-point format, also called FP32, is a computer number format that typically occupies 32 bits in PC memory. This represents a wide dynamic range of numeric values that employs a floating point. FP32 power helps assess the video card's capabilities for generating rendering workloads. Video games use INT32 and FP32 cores to manage calculations with integers and numbers with decimals in order to produce maps, bone matrices on which the vertex shaders will calculate the vertices. Values in single precision format are mainly used in video games and 3D software.

FP64 performance comparison (double precision; float; floating-point 64) for this graphics card:

FP64 Performance in GFLOPS
AMD Radeon RX 6950 XT 1,487
AMD Radeon RX 6900 XT 1,440
AMD Radeon RX 6800 XT 1,296
AMD Radeon RX 6800 1,010.4
AMD Radeon RX 6750 XT 832
AMD Radeon RX 6700 XT 825.9
AMD Radeon RX 6700 705.6
AMD Radeon RX 6650 XT 674.6
AMD Radeon RX 6600 XT 662.8
AMD Radeon RX 6600 560
AMD Radeon RX 6500 XT 360.3
AMD Radeon RX 6400 192

FP64 performance refers to solvers with double precision. We can thus determine how many operations the GPU can calculate for certain specific tasks, such as scientific calculations, climate forecasts, calculations related to fluid dynamics, geophysics, computational chemistry. The FP64 power capabilities are mainly intended for professionals who wish to carry out advanced calculations.

List of comparisons:

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List of benchmarks:

FP32 Performance

See also:

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