Huawei unveils new Atlas 350 AI accelerator with 1.56 PFLOPS of FP4 compute and up to 112GB of HBM — claims 2.8x more performance than Nvidia’s H20

Huawei unveils new Atlas 350 AI accelerator with 1.56 PFLOPS of FP4 compute and up to 112GB of HBM — claims 2.8x more performance than Nvidia's H20

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China's mission to become entirely self-reliant in the field of artificial intelligence has reached a new milestone. Announced at the Huawei China Partner Conference 2026 in Shenzhen, the company has just unveiled its latest AI accelerator: the Atlas 350. This new NPU is based on an in-house Ascend 950PR chip, representing a significant upgrade over the last-gen Ascend 910-class silicon.

That's already a significant achievement because even Nvidia only recently started to support the format with its Blackwell GPUs. FP4 allows for larger models to be deployed on the same hardware while requiring less memory. Speaking of which, the Atlas 350 comes with 112GB of Huawei's proprietary HBM known as "HiBL 1.0."

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Even though the Ascend 950PR otherwise features 128 GB of memory with a 1.6 TB/s bandwidth, current reports for the Atlas 350 say it maxes out at 1.4 TB/s. The memory access granularity has been reduced from 512 bytes to just 128 bytes. It also supports 2 TB/s interconnect bandwidth using the new LingQu protocol, which is 2.5x higher than the previous Ascend 910 series. The Atlas 350 is rated at 600W, 200W more than the H20.

Precise availability wasn't announced — it rarely is with AI accelerators — but Huawei has kept its prior promise of a Q1 2026 release for the Ascend 950PR. BigGo Finance says the NPU is priced at 111,000 Yuan (~$16,000) versus Nvidia's H20 which can range from anywhere between $15,000 to $25,000 in the region. Street pricing doesn't really exist for AI GPUs, so take this particular bit with a grain of salt.

There are a lot more Ascend chips in the pipeline that we've covered in a dedicated article before . Despite the ambition to gain independence from foreign hardware, Chinese companies still source Nvidia GPUs (and not the nerfed ones), which makes sense considering how local silicon is not quite as competitive yet and because the CUDA software stack is so mature. Huawei's latest efforts, therefore, represent a serious step in trying to bridge that gap.

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