NVIDIA Tesla V100 FHHL
About GPU
The NVIDIA Tesla V100 FHHL GPU is a powerful and high-performance graphics processing unit designed for professional use. With a base clock speed of 937MHz and a boost clock speed of 1290MHz, this GPU can handle demanding workloads with ease.
One of the standout features of the Tesla V100 is its large 16GB memory size, which is powered by HBM2 memory type and runs at a clock speed of 810MHz. This ensures that the GPU can efficiently handle large datasets and complex calculations without experiencing any bottlenecks.
With 5120 shading units and 6MB of L2 cache, the Tesla V100 delivers exceptional parallel processing capabilities, making it ideal for tasks such as deep learning, scientific simulations, and data analytics. Additionally, the GPU has a TDP of 250W, allowing it to deliver high performance while remaining energy efficient.
The theoretical performance of the Tesla V100 is an impressive 13.21 TFLOPS, ensuring that it can handle even the most demanding workloads with ease. Overall, the NVIDIA Tesla V100 FHHL GPU is a top-of-the-line solution for professionals who require high-performance computing capabilities for their work. Whether it's for machine learning, data analytics, or scientific simulations, this GPU delivers the power and efficiency needed to tackle complex tasks effectively. While it may come at a premium price, the performance and reliability it offers make it a worthwhile investment for professionals in need of top-tier graphics processing power.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
March 2018
Model Name
Tesla V100 FHHL
Generation
Tesla
Base Clock
937MHz
Boost Clock
1290MHz
Bus Interface
PCIe 3.0 x16
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
810MHz
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.
829.4 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.
165.1 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.
412.8 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.
26.42 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.
6.605 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.
13.474
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
13.474
TFLOPS
Compared to Other GPU
FP32 (float)
/ TFLOPS