H800 Tensor Core GPU
The PCIe version and SXM version are all based on the reduction of double precision (FP64) and nvlink transmission rate. Other parameters are the same as H100. The weakening on FP64 mainly affects the application of H800 in supercomputing fields such as scientific computing, fluid computing, finite element analysis, etc. Deep learning and other applications mainly focus on single precision floating-point performance, which is not affected in most scenarios. The biggest impact is still the reduction on NVlink, but due to the upgrade in architecture, it is still much stronger than A800.
H800-GPU can provide high-performance, high bandwidth, and low latency cluster examples for large model training, autonomous driving, deep learning, and other applications. When facing the training of AI models with trillions of parameters, the previous training time was 11 days. However, with the support of H800, the training time of the new generation cluster can be shortened to 4 days, proving that th
H800-GPU can provide high-performance, high bandwidth, and low latency cluster examples for large model training, autonomous driving, deep learning, and other applications. When facing the training of AI models with trillions of parameters, the previous training time was 11 days. However, with the support of H800, the training time of the new generation cluster can be shortened to 4 days, proving that th
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H800-GPU can provide high-performance, high bandwidth, and low latency cluster examples for large model training, autonomous driving, deep learning, and other applications. When facing the training of AI models with trillions of parameters, the previous training time was 11 days. However, with the support of H800, the training time of the new generation cluster can be shortened to 4 days, proving that the latest generation H800 has higher performance than A800 and can process tasks at the fastest speed. This further proves that H800 has sufficient status and ability in the field of large model training.
