Chinese rival NVIDIA reveals data center graphics card with AI support and more

2023-12-20 20:03:00

The competition from NVIDIA in Chinese territory is moving. This is the Moore Threads, the largest among the green brand’s rivals in the country, which is launching a new graphics card focused on the data center segment. Not only that, but the component also promises high performance in artificial intelligence applications.

The model in question is the MTT S4000, which was announced for the high-end segment of the category and brings some interesting upgrades compared to previous versions. At least, if we take into account the specifications that were actually revealed by the company, as in the case of video memories.


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The MTT S4000 card comes with 48GB of GDDR6 VRAM, which represents an increase of 50% in relation to models from previous generations, which arrived with 32GB at the time of launch. In fact, these older cards were launched with the first generation of the MUSA architecture, a detail that was updated for the second generation on the new card.

Other relevant details include more TFLOPs, with 25, as well as the bandwidth memory, which reaches 768 GB/s. However, the TDP value that this card works with, its operating speeds or the GPU cores are not yet known. The MTT S3000, its predecessor, was launched with 4096 cores.


In comparison with NVIDIA models in this category, the Chinese manufacturer’s new card promises superior performance to the 2018 Tesla models. However, it still lags behind the Ampere generations, from 2020, and Ada Lovelace, from 2022. In terms of AI, the board comes with the so-called KUAE solution, which supports language models like GPT and frameworks like DeepSpeed.

Despite not proving to be a rival on equal terms with NVIDIA and AMD, the MooreThreads product is still an interesting attempt to attract attention in the video card market for data centers.

See too: the launch of an NVIDIA GPU specific to China and the probable presentation date of the RTX 3050 card with 6GB of VRAM.

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