How Baidu’s Qianfan 2.0 Supercharges Large‑Model Development and Deployment

The speech by Xin Zhou at the 2023 Baidu Cloud Intelligence Conference introduced the Qianfan Large‑Model Platform 2.0, a comprehensive AI service that aims to shorten the time‑to‑value and reduce the cost of building and deploying large models.

Historical Analogy

The talk began with three historical questions—who invented the steam engine, the generator, and the first computer—to illustrate a common pattern: a breakthrough invention is followed by efficiency improvements, cost reductions, and mass adoption. The same pattern now applies to large‑model technology.

Qianfan 1.0 Achievements

Since the launch of Qianfan 1.0 on March 27, the platform attracted over 10,000 enterprises and developers, supported more than 400 use‑case scenarios, and delivered industry‑specific solutions for government, finance, manufacturing, transportation, and other sectors.

Key Upgrades in 2.0

Qianfan 2.0 expands the model catalog to 42 distinct large models, including Baidu’s Wenxin, the domestic ChatGLM, the long‑context RWKV, and international open‑source models such as BLOOMZ and Llama 2. It also provides 41 curated datasets covering general, domain‑specific, and instruction data for education, finance, law, and more.

Chinese‑language enhancement applied to models like Llama 2 yields more than a 10 % improvement across evaluation metrics at both 7 B and 13 B parameter scales.

Model compression reduces model size by over 60 % and inference speed can increase up to 5×, dramatically lowering resource consumption for real‑world applications.

Additional capabilities include instruction tuning, performance boosting, 32 K context windows, and safety enhancements to meet diverse enterprise needs.

Robust AI Infrastructure (IaaS)

The underlying Baidu Baige (百舸) infrastructure delivers high‑performance, stable AI compute. Training stability reaches a 95 % effective‑time ratio, fault‑detection mechanisms identify task exits, deadlocks, and slow runs within seconds, and automatic checkpointing improves effective training time by 10 %.

Application Template (Sample Room)

A ready‑to‑use sample room demonstrates end‑to‑end domain‑knowledge enhancement. User queries pass through an API gateway, are decomposed into sub‑tasks via static chain or dynamic agent, optionally undergo prompt optimization, and invoke vector‑search on Baidu Elasticsearch (BES) for domain knowledge retrieval. Results from sub‑tasks are aggregated by the LLM, filtered by a content‑safety module, and returned to the user.

The template also provisions essential enterprise services such as key management and log management, enabling rapid construction of generative‑AI applications.

Demo: Building a Generative AI App in 7 Hours

A summer intern recorded a video showing how the Qianfan platform enabled the rapid construction of a data‑analysis product. The demo covered two tasks: instruction fine‑tuning from natural language to SQL, and domain‑specific Q&A using knowledge retrieval.

AI‑Native Applications and Full‑Site API

Dr. Shen Dou announced the AI‑native application "Family", which bundles Qianfan components for service marketing, office efficiency, and production optimization. The full‑site API allows enterprises to integrate Qianfan models and toolchains into their own products.

For example, the BI tool Sugar BI leverages Wenxin’s API to provide conversational data exploration, generating charts and insights on demand.

Data‑Labeling Platform

Baidu’s in‑house data‑labeling platform closes the last mile of large‑model deployment. It supports the full data lifecycle—from collection and cleaning, through instruction fine‑tuning and reinforcement‑learning‑from‑human‑feedback, to model evaluation. Hundreds of dedicated annotators ensure high‑quality labels, and the platform offers private‑deployment options for data security.

Future Outlook

To date, Qianfan has co‑created over 400 scenarios across technology, finance, energy, government, and more. The platform continues to expand model‑hardware compatibility, improve resource utilization, and accelerate industry‑wide AI adoption.

In summary, Qianfan 2.0 delivers a unified stack—models, datasets, toolchains, infrastructure, and APIs—that lowers development cost, speeds up deployment, and drives large‑model innovation across enterprises.