Home / GPU Comparison / NVIDIA Tesla C2075 or NVIDIA GeForce GTX 1650 TU116: What's better?

NVIDIA Tesla C2075

vs

NVIDIA GeForce GTX 1650 TU116

GPU Comparison Result

Below are the results of a comparison of NVIDIA Tesla C2075 and NVIDIA GeForce GTX 1650 TU116 video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA Tesla C2075

Larger Memory Size: 6GB (6GB vs 4GB)

NVIDIA GeForce GTX 1650 TU116

Higher Bandwidth: 192.0 GB/s (150.3 GB/s vs 192.0 GB/s)
More Shading Units: 896 (448 vs 896)
Newer Launch Date: July 2020 (July 2011 vs July 2020)

Basic

NVIDIA

Label Name

NVIDIA

July 2011

Launch Date

July 2020

Professional

Platform

Desktop

Tesla C2075

Model Name

GeForce GTX 1650 TU116

Tesla

Generation

GeForce 16

Base Clock

1410MHz

Boost Clock

1590MHz

PCIe 2.0 x16

Bus Interface

PCIe 3.0 x16

3,000 million

Transistors

6,600 million

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.

TSMC

Foundry

TSMC

40 nm

Process Size

12 nm

Fermi 2.0

Architecture

Turing

Memory Specifications

6GB

Memory Size

4GB

GDDR5

Memory Type

GDDR6

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.

128bit

783MHz

Memory Clock

1500MHz

150.3 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.

192.0 GB/s

Display and Media

1x DVI

Outputs

1x DVI
1x HDMI 2.0
1x DisplayPort 1.4a

Theoretical Performance

16.07 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.

50.88 GPixel/s

32.14 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.

89.04 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.

5.699 TFLOPS

513.9 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.

89.04 GFLOPS

1.007 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.

2.792 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.

448

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.

896

64 KB (per SM)

L1 Cache

64 KB (per SM)

768KB

L2 Cache

1024KB

247W

TDP

80W

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

1.1

OpenCL Version

3.0

4.6

OpenGL

4.6

2.0

CUDA

7.5

12 (11_0)

DirectX

12 (12_1)

1x 6-pin + 1x 8-pin

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.

5.1

Shader Model

6.6

550W

Suggested PSU

250W