NVIDIA Tesla V100 PCIe 32 GB
About GPU
The NVIDIA Tesla V100 PCIe 32GB GPU is an absolute powerhouse for professional workloads. With a base clock speed of 1230MHz and a boost clock speed of 1380MHz, this GPU is capable of handling even the most demanding tasks with ease.
With a massive 32GB of HBM2 memory and a memory clock of 876MHz, the V100 PCIe 32GB can easily handle large datasets and complex simulations. The 5120 shading units and 6MB of L2 cache ensure that even the most complex calculations can be processed quickly and efficiently. The TDP of 250W may seem high, but it is a necessary tradeoff for the sheer amount of performance this card can deliver.
In terms of raw performance, the V100 PCIe 32GB is capable of delivering a theoretical 14.13 TFLOPS, making it one of the most powerful GPUs on the market. This level of performance makes it an ideal choice for deep learning, AI, data analytics, and other professional workloads that require massive parallel processing power.
Overall, the NVIDIA Tesla V100 PCIe 32GB GPU is an absolute beast of a card. While it may be overkill for the average consumer, for professionals who need the absolute best performance for their workloads, the V100 PCIe 32GB is an excellent choice. Its massive memory, high clock speeds, and impressive number of shading units make it an incredibly powerful tool for tackling the most demanding tasks.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
March 2018
Model Name
Tesla V100 PCIe 32 GB
Generation
Tesla
Base Clock
1230MHz
Boost Clock
1380MHz
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.
176.6 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.
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
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
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.
13.847
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
13.847
TFLOPS
Blender
Score
2297
OctaneBench
Score
319
Compared to Other GPU
FP32 (float)
/ TFLOPS
Blender
OctaneBench