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NVIDIA GeForce GTX 1050 Mobile 3 GB

vs

AMD Radeon 780M

GPU Comparison Result

Below are the results of a comparison of NVIDIA GeForce GTX 1050 Mobile 3 GB and AMD Radeon 780M video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA GeForce GTX 1050 Mobile 3 GB

Larger Memory Size: 3GB (3GB vs System Shared)
Higher Bandwidth: 84.10 GB/s (84.10 GB/s vs System Dependent)

AMD Radeon 780M

Higher Boost Clock: 2900MHz (1442MHz vs 2900MHz)
Newer Launch Date: January 2023 (February 2019 vs January 2023)

Basic

NVIDIA

Label Name

AMD

February 2019

Launch Date

January 2023

Mobile

Platform

Integrated

GeForce GTX 1050 Mobile 3 GB

Model Name

Radeon 780M

GeForce 10 Mobile

Generation

Navi III IGP

1366MHz

Base Clock

1500MHz

1442MHz

Boost Clock

2900MHz

PCIe 3.0 x16

Bus Interface

PCIe 4.0 x8

3,300 million

Transistors

25,390 million

RT Cores

Compute Units

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.

Samsung

Foundry

TSMC

14 nm

Process Size

4 nm

Pascal

Architecture

RDNA 3.0

Memory Specifications

3GB

Memory Size

System Shared

GDDR5

Memory Type

System Shared

96bit

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.

System Shared

1752MHz

Memory Clock

SystemShared

84.10 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.

System Dependent

Display and Media

No outputs

Outputs

Portable Device Dependent

Theoretical Performance

34.61 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.

92.80 GPixel/s

69.22 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.

139.2 GTexel/s

34.61 GFLOPS

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.

17.82 TFLOPS

69.22 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.

556.8 GFLOPS

2.259 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.

8.731 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.

768

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.

768

48 KB (per SM)

L1 Cache

128 KB per Array

768KB

L2 Cache

2MB

75W

TDP

15W

1.3

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

3.0

OpenCL Version

2.1

4.6

OpenGL

4.6

6.1

CUDA

12 (12_1)

DirectX

12 Ultimate (12_2)

None

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.

6.4

Shader Model

6.7