NVIDIA Tesla V100S PCIe 32 GB
About GPU
The NVIDIA Tesla V100S PCIe 32GB GPU is a high-performance professional computing platform suitable for a wide range of demanding workloads. With a base clock speed of 1245MHz and a boost clock speed of 1597MHz, this GPU offers exceptional processing power for complex computational tasks.
One of the standout features of the Tesla V100S is its huge 32GB of HBM2 memory, allowing for the rapid processing of large datasets and high-resolution graphics. The memory clock speed of 1106MHz further enhances its capability to handle data-intensive applications.
With 5120 shading units and 6MB of L2 cache, the Tesla V100S delivers outstanding parallel processing performance, making it well-suited for deep learning, AI, scientific simulations, and other compute-intensive workloads. The 250W TDP ensures efficient power usage, and the theoretical performance of 16.35 TFLOPS showcases its ability to handle complex calculations with ease.
The Tesla V100S PCIe 32GB GPU is designed for professionals who require reliable, high-performance computing power for their work. Whether it's accelerating scientific research, powering AI and machine learning applications, or running complex simulations, this GPU offers the performance and memory capacity necessary to handle the most demanding tasks.
Overall, the NVIDIA Tesla V100S PCIe 32GB GPU is a powerhouse for professional users who require top-tier performance and memory capacity for their advanced computing needs. Its impressive specifications make it a standout choice for professionals working in fields such as data science, engineering, and research.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
November 2019
Model Name
Tesla V100S PCIe 32 GB
Generation
Tesla
Base Clock
1245MHz
Boost Clock
1597MHz
Bus Interface
PCIe 3.0 x16
Memory Specifications
Memory Size
32GB
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
Benchmarks
FP32 (float)
Score
16.023
TFLOPS
Blender
Score
2328
Compared to Other GPU
FP32 (float)
/ TFLOPS
Blender