Home / GPU Comparison / AMD Radeon R9 M295X Mac Edition or AMD Radeon R9 M370X Mac Edition: What's better?

AMD Radeon R9 M295X Mac Edition

vs

AMD Radeon R9 M370X Mac Edition

GPU Comparison Result

Below are the results of a comparison of AMD Radeon R9 M295X Mac Edition and AMD Radeon R9 M370X Mac Edition video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

AMD Radeon R9 M295X Mac Edition

Larger Memory Size: 4GB (4GB vs 2GB)
Higher Bandwidth: 174.3 GB/s (174.3 GB/s vs 72.00 GB/s)
More Shading Units: 2048 (2048 vs 640)

AMD Radeon R9 M370X Mac Edition

Newer Launch Date: May 2015 (November 2014 vs May 2015)

Basic

AMD

Label Name

AMD

November 2014

Launch Date

May 2015

Mobile

Platform

Mobile

Radeon R9 M295X Mac Edition

Model Name

Radeon R9 M370X Mac Edition

Crystal System

Generation

Gem System

Base Clock

775MHz

Boost Clock

800MHz

MXM-B (3.0)

Bus Interface

PCIe 3.0 x16

5,000 million

Transistors

1,500 million

Compute Units

128

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

28 nm

GCN 3.0

Architecture

GCN 1.0

Memory Specifications

4GB

Memory Size

2GB

GDDR5

Memory Type

GDDR5

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.

128bit

1362MHz

Memory Clock

1125MHz

174.3 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.

72.00 GB/s

Display and Media

No outputs

Outputs

Portable Device Dependent

Theoretical Performance

27.20 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.

12.80 GPixel/s

108.8 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.

32.00 GTexel/s

3.482 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.

217.6 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.

64.00 GFLOPS

3.552 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.

1.004 TFLOPS

Miscellaneous

2048

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.

640

16 KB (per CU)

L1 Cache

16 KB (per CU)

512KB

L2 Cache

256KB

250W

TDP

Unknown

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.

1.2.170

2.0

OpenCL Version

2.1 (1.2)

4.6

OpenGL

4.6

12 (12_0)

DirectX

12 (11_1)

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

6.5 (5.1)

Benchmarks

FP32 (float) / TFLOPS