Huawei Released openPangu 2.0 (Will open source on June 30)

At the Huawei Developer Conference (HDC 2026) held on June 12, Richard Yu, Executive Director of Huawei, officially launched the brand-new, open-source Pangu large model—openPangu 2.0. The model is fully adapted to the HarmonyOS ecosystem and has achieved deep optimization and performance breakthroughs on Ascend computing power.

openPangu 2.0 features a 512K context processing capability and comes in two versions tailored for different application scenarios. It sets a record for the largest sparsity ratio in the hundred-billion-parameter category at 28:1:

- openPangu 2.0 Pro: Total parameters: 505B ; Activated parameters: 18B.

- openPangu 2.0 Flash: Total parameters: 92B ; Activated parameters: 6B.

According to the conference presentations and live demonstrations, openPangu 2.0 has been comprehensively upgraded in throughput, latency, and task processing:

• Highly optimized for Ascend computing power, its single-card user throughput is up to 2x that of mainstream open-source models in the industry.
• Built on Ascend-native training, hyper-node optimized training efficiency has improved by 30%, 512K long-sequence training throughput has increased by 50%, and training consistency exceeds 99%.
• Utilizes a high-precision architecture (mHC | Muon | ModAttn) and pioneers the DSA+SWA independent layered hybrid architecture (ultra-sparse attention) for more precise computing power allocation.

Huawei announced plans to progressively open-source the core components of openPangu 2.0 starting June 30, fully empowering developers:

Basic Components: Model architecture, model weights, technical reports, and inference code.

Newly Open-Sourced Components: Pre-training code, post-training code, and training operators.

Addressing the public attention surrounding the 505B total parameter count of the 2.0 Pro version, Richard Yu explained at the conference that this design is due to Huawei allocating a vast amount of its computing power to support the needs of other china enterprises, leaving limited computing power for itself. Furthermore, considering the exorbitant costs of AI computing, Huawei's current strategy ocuses more heavily on achieving substantial improvements in latency and throughput rate.

(Image used Nano banana 2 to translate the image to English)