NVIDIA Tesla V100 SXM2 16 GB
The NVIDIA Tesla V100 SXM2 16 GB GPU is a powerhouse in the world of professional computing. With an impressive 16GB of HBM2 memory, a base clock of 1245MHz, and a boost clock of 1597MHz, this GPU delivers unparalleled performance for demanding workloads such as deep learning, scientific simulations, and artificial intelligence.
One of the standout features of the Tesla V100 is its 5120 shading units, which enable it to handle complex calculations and data processing with ease. The 6MB L2 cache also plays a crucial role in optimizing performance and ensuring smooth operation even under heavy workloads.
In terms of power efficiency, the Tesla V100 excels with a TDP of 250W, allowing it to deliver high performance without consuming excessive amounts of power. This makes it an excellent choice for data centers and professional computing environments where energy efficiency is a priority.
With a theoretical performance of 16.35 TFLOPS, the Tesla V100 is capable of tackling the most demanding computational tasks with ease. Whether you're working on machine learning models, computational fluid dynamics simulations, or other data-intensive applications, this GPU is more than up to the task.
Overall, the NVIDIA Tesla V100 SXM2 16 GB GPU is a top-of-the-line solution for professionals in need of high-performance computing power. Its impressive specs and efficient design make it a clear choice for anyone looking to take their computational work to the next level.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
November 2019
Model Name
Tesla V100 SXM2 16 GB
Generation
Tesla
Base Clock
1245MHz
Boost Clock
1597MHz
Bus Interface
PCIe 3.0 x16
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
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
Foundry
TSMC
Process Size
12 nm
Architecture
Volta
Memory Specifications
Memory Size
16GB
Memory Type
HBM2
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
Memory Clock
1106MHz
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.
1133 GB/s
Theoretical Performance
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.
204.4 GPixel/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.
511.0 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.
32.71 TFLOPS
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.
8.177 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.
16.023
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.
80
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
L1 Cache
128 KB (per SM)
L2 Cache
6MB
TDP
250W
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
OpenCL Version
3.0
OpenGL
4.6
DirectX
12 (12_1)
CUDA
7.0
Power Connectors
None
Shader Model
6.6
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
Suggested PSU
600W
Benchmarks
FP32 (float)
Score
16.023
TFLOPS
Blender
Score
2481
OctaneBench
Score
361
Compared to Other GPU
FP32 (float)
/ TFLOPS
Blender
OctaneBench
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