AMD Radeon R9 280X vs NVIDIA GeForce GT 1030
GPU Comparison Result
Below are the results of a comparison of
AMD Radeon R9 280X
and
NVIDIA GeForce GT 1030
video cards based on key performance characteristics, as well as power consumption and much more.
Advantages
- Larger Memory Size: 3GB (3GB vs 2GB)
- Higher Bandwidth: 288.0 GB/s (288.0 GB/s vs 48.06 GB/s)
- More Shading Units: 2048 (2048 vs 384)
- Higher Boost Clock: 1468MHz (1000MHz vs 1468MHz)
- Newer Launch Date: May 2017 (October 2013 vs May 2017)
Basic
AMD
Label Name
NVIDIA
October 2013
Launch Date
May 2017
Desktop
Platform
Desktop
Radeon R9 280X
Model Name
GeForce GT 1030
Volcanic Islands
Generation
GeForce 10
850MHz
Base Clock
1228MHz
1000MHz
Boost Clock
1468MHz
PCIe 3.0 x16
Bus Interface
PCIe 3.0 x4
4,313 million
Transistors
1,800 million
32
Compute Units
-
128
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.
24
TSMC
Foundry
Samsung
28 nm
Process Size
14 nm
GCN 1.0
Architecture
Pascal
Memory Specifications
3GB
Memory Size
2GB
GDDR5
Memory Type
GDDR5
384bit
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.
64bit
1500MHz
Memory Clock
1502MHz
288.0 GB/s
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.
48.06 GB/s
Theoretical Performance
32.00 GPixel/s
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.
23.49 GPixel/s
128.0 GTexel/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.
35.23 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.
17.62 GFLOPS
1024 GFLOPS
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.
35.23 GFLOPS
4.014
TFLOPS
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.
1.104
TFLOPS
Miscellaneous
-
SM Count
?
Multiple Streaming Processors (SPs), along with other resources, form a Streaming Multiprocessor (SM), which is also referred to as a GPU's major core. These additional resources include components such as warp schedulers, registers, and shared memory. The SM can be considered the heart of the GPU, similar to a CPU core, with registers and shared memory being scarce resources within the SM.
3
2048
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.
384
16 KB (per CU)
L1 Cache
48 KB (per SM)
768KB
L2 Cache
512KB
250W
TDP
30W
1.2
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.
1.3
1.2
OpenCL Version
3.0
4.6
OpenGL
4.6
12 (11_1)
DirectX
12 (12_1)
-
CUDA
6.1
1x 6-pin + 1x 8-pin
Power Connectors
None
32
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.
16
5.1
Shader Model
6.4
600W
Suggested PSU
200W
Benchmarks
FP32 (float)
/ TFLOPS
Radeon R9 280X
4.014
+264%
GeForce GT 1030
1.104
3DMark Time Spy
Radeon R9 280X
2394
+117%
GeForce GT 1030
1105
Hashcat
/ H/s
Radeon R9 280X
151963
+185%
GeForce GT 1030
53248