NVIDIA Tesla M2075
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NVIDIA Tesla V100 PCIe 16 GB

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GPU Comparison Result

Below are the results of a comparison of NVIDIA Tesla M2075 and NVIDIA Tesla V100 PCIe 16 GB video cards based on key performance characteristics, as well as power consumption and much more.

Advantages

  • Larger Memory Size: 16GB (6GB vs 16GB)
  • Higher Bandwidth: 897.0 GB/s (150.3 GB/s vs 897.0 GB/s)
  • More Shading Units: 5120 (448 vs 5120)
  • Newer Launch Date: June 2017 (July 2011 vs June 2017)

Basic

NVIDIA
Label Name
NVIDIA
July 2011
Launch Date
June 2017
Professional
Platform
Professional
Tesla M2075
Model Name
Tesla V100 PCIe 16 GB
Tesla
Generation
Tesla
-
Base Clock
1245MHz
-
Boost Clock
1380MHz
PCIe 2.0 x16
Bus Interface
PCIe 3.0 x16
3,000 million
Transistors
21,100 million
-
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.
640
56
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.
320
TSMC
Foundry
TSMC
40 nm
Process Size
12 nm
Fermi 2.0
Architecture
Volta

Memory Specifications

6GB
Memory Size
16GB
GDDR5
Memory Type
HBM2
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.
4096bit
783MHz
Memory Clock
876MHz
150.3 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.
897.0 GB/s

Display and Media

No outputs
Outputs
No outputs

Theoretical Performance

16.07 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.
176.6 GPixel/s
32.14 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.
441.6 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.
28.26 TFLOPS
513.9 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.
7.066 TFLOPS
1.007 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.
14.413 TFLOPS

Miscellaneous

14
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.
80
448
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.
5120
64 KB (per SM)
L1 Cache
128 KB (per SM)
768KB
L2 Cache
6MB
225W
TDP
300W
-
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
1.1
OpenCL Version
3.0
4.6
OpenGL
4.6
2.0
CUDA
7.0
12 (11_0)
DirectX
12 (12_1)
1x 6-pin + 1x 8-pin
Power Connectors
2x 8-pin
48
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.
128
5.1
Shader Model
6.6
550W
Suggested PSU
700W

Benchmarks

FP32 (float) / TFLOPS
Tesla M2075
1.007
Tesla V100 PCIe 16 GB
14.413 +1331%