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The Atlas 960E SuperPoD targets 10-trillion-parameter models with up to 16 EFLOPS of compute and a petabyte of high-bandwidth memory
Huawei just made its boldest play yet in the AI hardware arms race. At HUAWEI CONNECT 2026 in Shanghai on September 17, the company revealed the Atlas 960E SuperPoD, an AI computing cluster that stitches together 4,096 of its Ascend neural processing units into what it describes as a single logical machine with unified memory addressing.
Think of it like this: instead of 4,096 separate brains trying to coordinate over walkie-talkies, the system makes them behave like one enormous brain sharing one enormous memory. That distinction matters a lot when you’re training models with up to 10 trillion parameters.
The numbers behind the machine #
The Atlas 960E delivers 8 EFLOPS at FP8 precision and 16 EFLOPS at FP4 precision. The full configuration supports up to 1 petabyte of high-bandwidth memory, which is roughly the equivalent of storing 250 million high-resolution photos in the fastest memory tier available.
To keep all those chips talking to each other without bottlenecks, Huawei developed what it calls the Hi-ONE Near-Packaged Optics engine. It’s an industry first, according to the company, pushing 7.2 terabits per second per module. That optical interconnect technology also cuts power consumption by more than 550 kilowatts per pod.
Compared to previous Atlas generations, the 960E architecture is projected to deliver between 2.3 and 4 times the training and inference throughput for large models. Huawei also claims a 99.8% operational availability rate.
The company didn’t stop at the SuperPoD announcement. It revealed that the Ascend 960DT training chip, originally slated for a later release, has been pulled forward to Q1 2027, arriving three quarters ahead of schedule.
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Scaling ambitions and the competitive landscape #
Perhaps the most striking long-term detail is Huawei’s stated goal of scaling to 1 million NPUs using multi-rail topology.
Alongside the Atlas 960E, Huawei also announced upgrades to its TaiShan 950 SuperPoD and introduced the OceanStor M900 storage solution.
Why the power efficiency angle matters more than it seems #
The 550 kW power reduction per pod deserves more attention than a bullet point. AI data centers are increasingly bumping up against physical power constraints, with new facilities being built next to power plants and some regions running out of available grid capacity for new data center construction.
The 10-trillion-parameter target is worth contextualizing. Today’s largest publicly known models sit in the range of a few trillion parameters. By targeting 10 trillion, Huawei is building hardware for models that don’t widely exist yet.
The accelerated timeline for the Ascend 960DT chip adds another layer of competitive pressure. Pulling a major chip launch forward by three quarters compresses the window between announcement and availability, giving potential customers less reason to commit to alternative platforms while waiting.
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