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NVIDIA H800 PCIE Computing accelerator

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NVIDIA H800 PCIE

The NVIDIA H800 PCIe module is another high-performance computing accelerator from NVIDIA, also designed for data centers. It is similar to the H100 in many ways, but also has its own unique features.

Product name


NVIDIA H800 PCIe: Like the H100, the name intuitively points to its PCIe interface-based features.
Product description


The H800 PCIe module is designed for a variety of applications that require high performance computing. It provides powerful computing power and is suitable for a wide range of workloads.

Excellent performance: The H800 delivers high-performance computing power that accelerates a variety of compute-intensive tasks.
High-bandwidth memory: Equipped with large-capacity high-bandwidth HBM2e video memory, capable of processing massive data.
Versatility: Supports multiple precision calculations for a variety of workloads.
Scalability: NVLink technology enables multi-GPU interconnection to build a powerful computing cluster.
Energy efficient: While delivering powerful performance, the H800 incorporates advanced energy saving technologies.
Product parameters and specifications
The specific parameters of the H800 PCIe module vary slightly depending on the model, but generally include the following key indicators:

The number of CUDA cores determines the parallel computing capability of the GPU.
Video memory capacity: affects the GPU’s ability to process data.
Video bandwidth: Determines the speed of data transfer between the GPU and memory.
Power consumption: Indicates the power consumption level of the GPU.
Ports: The H800 PCIe module usually uses PCIe 4.0 ports.
Supported features such as Tensor Cores, RT Cores, and Multi-instance Gpus (MIG).
Please note: The H800’s performance and specifications may be slightly inferior compared to the H100, but it is still a very powerful computing accelerator.

Product advantage
High performance computing: suitable for scientific computing, engineering simulation, financial analysis and other fields.
AI acceleration: Can be used to accelerate AI model training and reasoning.
Data Analytics: Accelerate large-scale data processing and analysis.




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