NVIDIA Tesla T4 vs NVIDIA Tesla P100 PCIe 16 GB
GPU Comparison Result
Below are the results of a comparison of
NVIDIA Tesla T4
and
NVIDIA Tesla P100 PCIe 16 GB
video cards based on key performance characteristics, as well as power consumption and much more.
Advantages
- Higher Boost Clock: 1590MHz (1590MHz vs 1329MHz)
- Newer Launch Date: September 2018 (September 2018 vs June 2016)
- Higher Bandwidth: 732.2 GB/s (320.0 GB/s vs 732.2 GB/s)
- More Shading Units: 3584 (2560 vs 3584)
Basic
NVIDIA
Label Name
NVIDIA
September 2018
Launch Date
June 2016
Professional
Platform
Professional
Tesla T4
Model Name
Tesla P100 PCIe 16 GB
Tesla
Generation
Tesla
585MHz
Base Clock
1190MHz
1590MHz
Boost Clock
1329MHz
PCIe 3.0 x16
Bus Interface
PCIe 3.0 x16
13,600 million
Transistors
15,300 million
40
RT Cores
-
320
Tensor Cores
?
Tensor Cores are specialized processing units designed specifically for deep learning, providing higher training and inference performance compared to FP32 training. They enable rapid computations in areas such as computer vision, natural language processing, speech recognition, text-to-speech conversion, and personalized recommendations. The two most notable applications of Tensor Cores are DLSS (Deep Learning Super Sampling) and AI Denoiser for noise reduction.
-
160
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.
224
TSMC
Foundry
TSMC
12 nm
Process Size
16 nm
Turing
Architecture
Pascal
Memory Specifications
16GB
Memory Size
16GB
GDDR6
Memory Type
HBM2
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.
4096bit
1250MHz
Memory Clock
715MHz
320.0 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.
732.2 GB/s
Theoretical Performance
101.8 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.
127.6 GPixel/s
254.4 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.
297.7 GTexel/s
65.13 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.
19.05 TFLOPS
254.4 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.
4.763 TFLOPS
8.304
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.
9.335
TFLOPS
Miscellaneous
40
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.
56
2560
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.
3584
64 KB (per SM)
L1 Cache
24 KB (per SM)
4MB
L2 Cache
4MB
70W
TDP
250W
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.
1.3
3.0
OpenCL Version
3.0
4.6
OpenGL
4.6
7.5
CUDA
6.0
12 Ultimate (12_2)
DirectX
12 (12_1)
None
Power Connectors
1x 8-pin
6.6
Shader Model
6.4
64
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.
96
250W
Suggested PSU
600W
Benchmarks
FP32 (float)
/ TFLOPS
Tesla T4
8.304
Tesla P100 PCIe 16 GB
9.335
+12%
Blender
Tesla T4
1693
+41%
Tesla P100 PCIe 16 GB
1200
OctaneBench
Tesla T4
159
Tesla P100 PCIe 16 GB
217
+36%