How Large Models Are Redefining Cloud Computing: The AI‑Native Revolution

1  AI Native Era Opens, Large Models Drive Cloud Innovation

Large models are not just an AI breakthrough; they are prompting a fundamental redesign of underlying IT infrastructure and transforming application development patterns.

Since 2012, deep learning has become the mainstream AI algorithm, powering mobile applications but without changing the development paradigm. Classic cloud computing introduced virtualization, making compute a basic service, while mobile apps adopted cloud‑native designs to boost iteration speed.

These three domains—mobile, deep learning, and cloud—evolved in parallel. The large‑model era finally brings them together.

At the application layer, large models enable AI‑native apps that understand, generate, reason, and remember. They also become a generic service (MaaS), lowering AI adoption barriers and achieving true AI democratization. Meanwhile, cloud computing evolves into an AI‑native cloud, reshaping the industry landscape.

2  Generative AI Sparks a New R&D Paradigm: New Architecture, New Services, New Computing

Generative AI requires new architectural guidance, new service models, and new compute infrastructures.

Model : Model capabilities are offered as API services, including both base models and fine‑tuned customer models.

Prompt : Prompts help users obtain better model responses.

Chain and Agent : Enable static and dynamic orchestration, leveraging large‑model abilities for chained calls.

These components reshape data and business flows in AI‑native applications.

New Services : Model capabilities become a new foundational service (MaaS) characterized by richness, ease of use, and AI‑native orientation. The platform must provide a diverse model catalog, a complete toolchain covering data collection, annotation, model training, evaluation, and deployment, and support data‑closed‑loop capabilities for continuous model iteration.

New Computing : Large models demand massive, high‑density computation, driving a shift toward heterogeneous computing, larger scales, and microsecond‑level interconnects. A holistic hardware‑software approach is required.

3  Prompt the Future: Simplifying AI‑Native App Development

An AI‑native app starts with a user request, which is decomposed into sub‑tasks (e.g., domain knowledge enhancement, search augmentation). These sub‑tasks are processed by large language models, combined into a final response, and passed through a safety audit before returning to the user. The underlying infrastructure supports orchestration, debugging, and logging.

Key capabilities needed include a low‑code development environment with visual debugging tools, rich domain‑enhancement services (vector databases, data lakes, search), and comprehensive content safety mechanisms.

4  Model Platform Highlights

The Qianfan Large Model Platform 2.0 offers multiple high‑quality models (including Baidu’s Wenxin Yiyan and third‑party models), visual tools for fine‑tuning, a rich ecosystem of auxiliary tools, and pre‑built datasets that feed back into model iteration.

5  Infrastructure Advances: Baidu’s Baige Heterogeneous Computing Platform

Baige provides stable, high‑reliability systems, high‑performance training and inference services, and a self‑developed high‑speed network for low latency and high throughput.

It delivers strong fault tolerance (second‑level fault detection, minute‑level automatic recovery) and supports massive parallel training without interruption, achieving over 30% training performance gains and ten‑fold inference throughput improvements for public model libraries.

6  AI‑Native Cloud Product Panorama

Baidu Intelligent Cloud has built a comprehensive AI‑native cloud product suite, of which the discussed content is only a part.

7  AI for All

Baidu is committed to making AI accessible to every individual and organization, breaking digital divides and simplifying complex problems through technology.