Huawei’s Power‑Hungry Alternative to NVIDIA’s GB200

Huawei’s AI CloudMatrix has quickly gained attention as a potent—but energy‑intensive—competitor to NVIDIA’s GB200 NVL72 accelerator. Although it consumes roughly four times the power of the GB200, this trade‑off isn’t deterring many Chinese enterprises.

Why the High Power Draw?

Rather than relying on cutting‑edge fabrication, Huawei leans on “brute‑force” scaling: packing more AI processors onto each board to boost throughput. Since export restrictions limit access to the latest process nodes, the company compensates with volume, deploying multiple chiplets in concert—at the cost of higher electricity consumption.

Inside the CloudMatrix Architecture

• Dual‑Chiplet Ascend 910C Modules: Each accelerator board hosts two Ascend 910C chiplets.

• Optical Interconnects: Intra‑ and inter‑rack connections use high‑bandwidth fiber optics instead of copper, maximizing data flow across the cluster.

• Cluster Scale: A total of 384 chiplets are arranged in an all‑to‑all optical mesh spanning 16 racks (12 compute racks with 32 accelerators each, plus 4 networking racks).

Performance vs. Efficiency

• Compute Power: Delivers ~300 TFLOPS of dense BF16 performance—twice that of the GB200.

• Memory & Bandwidth:

  • 2.1× more total memory capacity

  • 2.1× higher scale‑up bandwidth

  • 5.3× greater scale‑out throughput

  • 3.6× larger HBM pool

• Efficiency Trade‑Off:

  • 2.3× lower TFLOPS-per-watt

  • 1.8× less memory‑bandwidth efficiency (per TB/s)

  • 1.11× reduced HBM efficiency (per TB)

Power Requirements and Cost Impact

• Total Power Draw: Approximately 559 kW for a full CloudMatrix deployment versus just 145 kW for an equivalent GB200 setup.

• Electricity Costs: At China’s average industrial rate (~$56 / MWh), the additional power expense is modest relative to the benefits of accelerated AI training.

The Upshot for Chinese AI Workloads

Although CloudMatrix’s performance‑per‑watt lags NVIDIA’s GB200, its raw throughput and memory bandwidth make it a compelling option for large‑scale model training—especially when U.S. export controls restrict NVIDIA chip availability. With sufficient software orchestration and volume manufacturing, Huawei’s “brute‑force” strategy could become a practical foundation for China’s AI infrastructure.