Home / GPU Comparison / NVIDIA GeForce GT 1030 or NVIDIA GeForce RTX 4070 SUPER: What's better?

NVIDIA GeForce GT 1030 vs NVIDIA GeForce RTX 4070 SUPER

NVIDIA GeForce GT 1030

NVIDIA GeForce RTX 4070 SUPER

GPU Comparison Result

Below are the results of a comparison of NVIDIA GeForce GT 1030 and NVIDIA GeForce RTX 4070 SUPER video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA GeForce RTX 4070 SUPER

Higher Boost Clock: 2610MHz (1468MHz vs 2610MHz)
Larger Memory Size: 12GB (2GB vs 12GB)
Higher Bandwidth: 504.2 GB/s (48.06 GB/s vs 504.2 GB/s)
More Shading Units: 7168 (384 vs 7168)
Newer Launch Date: January 2024 (May 2017 vs January 2024)

Basic

NVIDIA

Label Name

NVIDIA

May 2017

Launch Date

January 2024

Desktop

Platform

Desktop

GeForce GT 1030

Model Name

GeForce RTX 4070 SUPER

GeForce 10

Generation

GeForce 40

1228MHz

Base Clock

2310MHz

1468MHz

Boost Clock

2610MHz

PCIe 3.0 x4

Bus Interface

PCIe 4.0 x16

1,800 million

Transistors

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

14 nm

Process Size

Pascal

Architecture

Memory Specifications

2GB

Memory Size

12GB

GDDR5

Memory Type

GDDR6X

64bit

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.

192bit

1502MHz

Memory Clock

1313MHz

48.06 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.

504.2 GB/s

Theoretical Performance

23.49 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.

208.8 GPixel/s

35.23 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.

584.6 GTexel/s

17.62 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.

37.42 TFLOPS

35.23 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.

584.6 GFLOPS

1.104 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.

38.168 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.

384

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.

7168

48 KB (per SM)

L1 Cache

128 KB (per SM)

512KB

L2 Cache

48MB

30W

TDP

285W

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

3.0

4.6

OpenGL

12 (12_1)

DirectX

6.1

CUDA

None

Power Connectors

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

200W

Suggested PSU