NVIDIA Tesla V100 SXM2 32 GB
About GPU
The NVIDIA Tesla V100 SXM2 32GB GPU is a top-of-the-line professional platform graphics card that delivers unparalleled performance for data center and enterprise applications. With a base clock of 1290MHz and a boost clock of 1530MHz, this GPU is capable of handling the most demanding workloads with ease. The 32GB of HBM2 memory and a memory clock of 876MHz ensure that it can handle large datasets and memory-intensive tasks effectively.
With 5120 shading units and 6MB of L2 cache, the V100 SXM2 offers incredible parallel processing power, making it ideal for machine learning, AI, and high-performance computing tasks. The TDP of 250W may seem high, but it is a reasonable power consumption for the immense computational power it provides.
The theoretical performance of 15.67 TFLOPS ensures that the V100 SXM2 can handle complex calculations and simulations in a fraction of the time compared to other GPUs. Its performance is especially valuable for deep learning and scientific computing applications.
Overall, the NVIDIA Tesla V100 SXM2 32GB GPU is an exceptional choice for professionals and enterprises looking for a high-performance GPU for their data center or server. Its impressive specifications and powerful performance make it a great investment for those who require top-tier computing power for their most demanding workloads.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
March 2018
Model Name
Tesla V100 SXM2 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.
15.983
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
15.983
TFLOPS
OctaneBench
Score
348
Compared to Other GPU
FP32 (float)
/ TFLOPS
OctaneBench