Home / GPU Comparison / AMD FirePro W7100 or NVIDIA Quadro P2200: What's better?

AMD FirePro W7100

vs

NVIDIA Quadro P2200

GPU Comparison Result

Below are the results of a comparison of AMD FirePro W7100 and NVIDIA Quadro P2200 video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

AMD FirePro W7100

Larger Memory Size: 8GB (8GB vs 5GB)
More Shading Units: 1792 (1792 vs 1280)

NVIDIA Quadro P2200

Higher Bandwidth: 200.2 GB/s (160.0 GB/s vs 200.2 GB/s)
Newer Launch Date: June 2019 (August 2014 vs June 2019)

Basic

AMD

Label Name

NVIDIA

August 2014

Launch Date

June 2019

Desktop

Platform

Professional

FirePro W7100

Model Name

Quadro P2200

FirePro

Generation

Quadro

Base Clock

1000MHz

Boost Clock

1493MHz

PCIe 3.0 x16

Bus Interface

PCIe 3.0 x16

5,000 million

Transistors

4,400 million

Compute Units

112

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

28 nm

Process Size

16 nm

GCN 3.0

Architecture

Pascal

Memory Specifications

8GB

Memory Size

5GB

GDDR5

Memory Type

GDDR5X

256bit

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.

160bit

1250MHz

Memory Clock

1251MHz

160.0 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.

200.2 GB/s

Theoretical Performance

29.44 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.

59.72 GPixel/s

103.0 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.

119.4 GTexel/s

3.297 TFLOPS

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.

59.72 GFLOPS

206.1 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.

119.4 GFLOPS

3.231 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.

3.898 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.

1792

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.

1280

16 KB (per CU)

L1 Cache

48 KB (per SM)

512KB

L2 Cache

1280KB

150W

TDP

75W

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

2.0

OpenCL Version

3.0

4.6

OpenGL

4.6

CUDA

6.1

12 (12_0)

DirectX

12 (12_1)

1x 6-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.

6.3

Shader Model

6.4

450W

Suggested PSU

250W