Advanced Digital Entertainment AI: Huawei Pangu 3.0

During the recent Huawei Cloud Digital Entertainment AI Innovation Summit at ChinaJoy 2023 in Shanghai, Huawei Cloud showcased its latest creation, the Pangu 3.0 model, designed specifically for the digital entertainment sector. The event demonstrated the model's incredible capabilities and practical applications within the industry.

In the wake of Chat-GPT's success, large-scale AI models have revolutionized various fields, including film, gaming, audio, animation, and more. This development has brought tremendous value to the digital entertainment industry, opening up new possibilities for creators and businesses alike.

Huawei Cloud's Global Vice President of Marketing and Sales, Ji Lin, emphasized their dedication to technological innovation in the digital entertainment domain. The company offers comprehensive AI services, integrating cutting-edge technologies into the digital entertainment landscape. Huawei Cloud collaborates with clients and partners to explore innovative pathways, fostering creativity and growth within the industry.

Huawei Cloud's Ascend AI Cloud Service delivers powerful AI computing capabilities, boasting an impressive 2000P Flops per cluster. By leveraging this service, models demonstrate a 30% improvement in AI inference performance compared to industry standards. In large-scale model training scenarios, the efficiency surpasses mainstream GPUs by 1.1 times, providing customers with exceptional choices for training large-scale models effectively.

ThePangu 3.0 model operates on a three-tier architecture, catering to various industry applications. At the foundational L0 tier, Huawei Cloud offers five essential large-scale models, each with specific industry skills. The L1 tier provides customers with industry-general large-scale models trained on publicly available data and allows for the convenient training of proprietary large-scale models using their own datasets. Finally, at the L2 tier, Huawei Cloud offers customers more specialized models for specific use cases, ensuring quick and easy implementation.