NVIDIA Tesla V100 SXM3 32 GB
About GPU
The NVIDIA Tesla V100 SXM3 32GB GPU is a powerful and efficient solution designed for professional use. With a base clock of 1290MHz and a boost clock of 1530MHz, this GPU delivers exceptional performance for demanding workloads. The 32GB of HBM2 memory and a memory clock of 876MHz ensure that large datasets can be processed efficiently, making it well-suited for deep learning, artificial intelligence, and other data-intensive tasks.
With 5120 shading units and 6MB of L2 cache, the Tesla V100 SXM3 offers unparalleled processing capabilities, enabling users to tackle complex computational tasks with ease. Additionally, with a TDP of 250W, this GPU delivers high performance while maintaining energy efficiency.
The theoretical performance of 15.67 TFLOPS further demonstrates the computational power of this GPU, making it an ideal choice for professionals who require high-speed data processing and analysis.
Overall, the NVIDIA Tesla V100 SXM3 32GB GPU is a top-of-the-line solution for professionals working in fields such as machine learning, data analytics, and scientific computing. Its impressive specifications and robust performance make it a valuable asset for any organization looking to harness the power of accelerated computing. While the price may be a consideration for some, the performance and capabilities of this GPU make it a worthwhile investment for those with demanding computational needs.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
March 2018
Model Name
Tesla V100 SXM3 32 GB
Generation
Tesla
Base Clock
1290MHz
Boost Clock
1530MHz
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
876MHz
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
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.
195.8 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.
489.6 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.
31.33 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.
7.834 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.
15.357
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
15.357
TFLOPS
Compared to Other GPU
FP32 (float)
/ TFLOPS