Home / GPU Comparison / NVIDIA Quadro P1000 or AMD Radeon R7 370: What's better?

NVIDIA Quadro P1000

vs

AMD Radeon R7 370

GPU Comparison Result

Below are the results of a comparison of NVIDIA Quadro P1000 and AMD Radeon R7 370 video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA Quadro P1000

Higher Boost Clock: 1480MHz (1480MHz vs 975MHz)
Larger Memory Size: 4GB (4GB vs 2GB)
Newer Launch Date: February 2017 (February 2017 vs June 2015)

AMD Radeon R7 370

Higher Bandwidth: 179.2 GB/s (80.19 GB/s vs 179.2 GB/s)
More Shading Units: 1024 (640 vs 1024)

Basic

NVIDIA

Label Name

AMD

February 2017

Launch Date

June 2015

Professional

Platform

Desktop

Quadro P1000

Model Name

Radeon R7 370

Quadro

Generation

Pirate Islands

1266MHz

Base Clock

925MHz

1480MHz

Boost Clock

975MHz

PCIe 3.0 x16

Bus Interface

PCIe 3.0 x16

3,300 million

Transistors

2,800 million

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

28 nm

Pascal

Architecture

GCN 1.0

Memory Specifications

4GB

Memory Size

2GB

GDDR5

Memory Type

GDDR5

128bit

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.

256bit

1253MHz

Memory Clock

1400MHz

80.19 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.

179.2 GB/s

Display and Media

4x mini-DisplayPort 1.4a

Outputs

2x DVI
1x HDMI 1.4a
1x DisplayPort 1.2

Theoretical Performance

47.36 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.

31.20 GPixel/s

59.20 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.

62.40 GTexel/s

29.60 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.

59.20 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.

124.8 GFLOPS

1.932 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.

1.957 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.

640

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.

1024

48 KB (per SM)

L1 Cache

16 KB (per CU)

1024KB

L2 Cache

512KB

47W

TDP

110W

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.

1.2

3.0

OpenCL Version

1.2

4.6

OpenGL

4.6

6.1

CUDA

12 (12_1)

DirectX

12 (11_1)

None

Power Connectors

1x 6-pin

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

5.1

200W

Suggested PSU

300W