NVIDIA H100 CNX
About GPU
The NVIDIA H100 CNX GPU is a professional-grade graphics processing unit that offers unparalleled performance for demanding workloads and high-performance computing tasks. With a base clock speed of 690MHz and a boost clock speed of 1845MHz, the H100 CNX delivers exceptional processing power to handle complex simulations, data analytics, and AI inference tasks with ease.
One of the standout features of the H100 CNX is its massive 80GB of HBM2e memory, which enables seamless handling of large datasets and memory-intensive applications. The high memory clock speed of 1593MHz ensures quick access to data, while the 14592 shading units and 50MB of L2 cache further bolster the GPU's processing capabilities.
In terms of power efficiency, the H100 CNX is designed with a TDP of 350W, enabling it to deliver exceptional performance without compromising on power consumption. The GPU's theoretical performance of 53.84 TFLOPS further underscores its ability to handle compute-intensive workloads effectively.
When it comes to professional applications such as data science, engineering, and content creation, the NVIDIA H100 CNX GPU shines as a reliable and powerful solution. Its robust performance, high memory capacity, and advanced architecture make it well-suited for professionals who require uncompromising performance for their most demanding tasks.
Overall, the NVIDIA H100 CNX GPU sets a new standard for professional-grade graphics processing, delivering remarkable performance and efficiency for a wide range of applications. It is an ideal choice for professionals and organizations seeking a high-performance GPU for their most challenging workloads.
Basic
Label Name
NVIDIA
Platform
Professional
Launch Date
March 2022
Model Name
H100 CNX
Generation
Tesla Hopper
Base Clock
690MHz
Boost Clock
1845MHz
Bus Interface
PCIe 5.0 x16
Memory Specifications
Memory Size
80GB
Memory Type
HBM2e
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.
5120bit
Memory Clock
1593MHz
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.
2039 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.
44.28 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.
841.3 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.
215.4 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.
26.92 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.
52.763
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.
114
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.
14592
L1 Cache
256 KB (per SM)
L2 Cache
50MB
TDP
350W
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.
N/A
OpenCL Version
3.0
Benchmarks
FP32 (float)
Score
52.763
TFLOPS
Compared to Other GPU
FP32 (float)
/ TFLOPS