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NVIDIA Tesla K80

vs

NVIDIA Tesla P40

GPU Comparison Result

Below are the results of a comparison of NVIDIA Tesla K80 and NVIDIA Tesla P40 video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

NVIDIA Tesla P40

Higher Boost Clock: 1531MHz (824MHz vs 1531MHz)
Larger Memory Size: 24GB (12GB vs 24GB)
Higher Bandwidth: 694.3 GB/s (240.6 GB/s vs 694.3 GB/s)
More Shading Units: 3840 (2496 vs 3840)
Newer Launch Date: September 2016 (November 2014 vs September 2016)

Basic

NVIDIA

Label Name

NVIDIA

November 2014

Launch Date

September 2016

Professional

Platform

Professional

Tesla K80

Model Name

Tesla P40

Tesla

Generation

Tesla Pascal

562MHz

Base Clock

1303MHz

824MHz

Boost Clock

1531MHz

PCIe 3.0 x16

Bus Interface

PCIe 3.0 x16

7,100 million

Transistors

11,800 million

208

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.

240

TSMC

Foundry

TSMC

28 nm

Process Size

16 nm

Kepler 2.0

Architecture

Pascal

Memory Specifications

12GB

Memory Size

24GB

GDDR5

Memory Type

GDDR5X

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.

384bit

1253MHz

Memory Clock

1808MHz

240.6 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.

694.3 GB/s

Theoretical Performance

42.85 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.

147.0 GPixel/s

171.4 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.

367.4 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.

183.7 GFLOPS

1371 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.

367.4 GFLOPS

4.195 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.

11.995 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.

2496

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.

3840

16 KB (per SMX)

L1 Cache

48 KB (per SM)

1536KB

L2 Cache

3MB

300W

TDP

250W

1.1

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

4.6

3.7

CUDA

6.1

12 (11_1)

DirectX

12 (12_1)

1x 8-pin

Power Connectors

8-pin EPS

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

6.7

700W

Suggested PSU

600W