Trends, Applications, and Future Directions of Large Models and Inference Acceleration

In recent years, rapid advances in artificial intelligence—especially large models and inference acceleration—are profoundly reshaping the technical ecosystem across industries.

DataFun is preparing the DA Digital Intelligence Conference (Shanghai) for April 25‑26, 2025. During the preparation, multiple industry experts were interviewed to share technical and practical insights, which are organized in this article.

Through real cases and technical analysis, the article reveals how large models can be applied in business, and how intelligent data governance and agent collaboration can improve model accuracy and reliability.

1. Technology Trends: From Data Governance to Inference Acceleration

1. Maturation of data governance. After years of development, data governance has become mature, with consistent practices in cost, security, warehouse modeling, and data mapping. The next focus is leveraging large models to boost efficiency and intelligence, especially for complex data processing and long‑text reasoning.

2. Rise of inference acceleration. This technology is crucial for deploying large models, especially for low‑cost private deployments. Enterprises often need on‑premise solutions for security, while individual users focus on mobile and vehicle scenarios. Optimizing inference efficiency is key to balancing cost and performance, particularly for complex tasks such as chain‑of‑thought reasoning.

3. Model acceleration and lightweighting. Techniques such as quantization, hardware‑software co‑optimization, and architecture redesign (e.g., DeepSeek V3’s Mixture‑of‑Experts) activate only a subset of parameters per inference, dramatically reducing compute while preserving capability.

4. AI and big data convergence. "AI for Data" applies AI to governance, operations, etc., while "Data for AI" builds data architectures that support AI training and deployment. AI agents are expected to play larger roles in data analysis and tool development.

2. Application Scenarios: From Research to Industry

1. Research and education. Large models assist in brainstorming, mathematical computation, and act as AI study companions. RAG‑based learning assistants can generate high‑quality exercises and improve teaching efficiency.

2. Data governance. Large models enhance cost, security, and warehouse model governance by handling complex data and long‑text reasoning, significantly improving precision and efficiency.

3. Inference acceleration. Optimizing inference through quantization, co‑optimization, and architecture tweaks lowers costs while maintaining model effectiveness.

4. Multimodal agents. Front‑line applications on mobile and automotive devices combine image, text, and video inputs, requiring real‑time interaction with the environment.

5. Generative AI. Large models power AIGC for advertising, translation, AI bots (NPCs, customer service), data analysis, development efficiency, and system monitoring, driving rapid growth of use cases.

3. Future Directions: From AGI to Synthetic Data

1. Frontier AI research. Upcoming trends focus on inference acceleration, multimodal, embodied intelligence, and AI for Science. Techniques like Test‑Time Scaling will become hot research topics.

2. AGI outlook. According to OpenAI, AGI should surpass most humans in most domains; models o1 and possibly o3 are approaching this goal, with open‑source communities also contributing to progress.

3. Synthetic data. In high‑risk scenarios such as autonomous driving, synthetic data generated by automated tools can fill gaps, while intelligent methods improve dataset quality and hallucination detection.

4. Technical Challenges and Opportunities

1. Balancing cost and effect. Optimizing inference efficiency remains the core challenge for reducing large‑model expenses, especially for complex reasoning chains.

2. Intelligent data governance. Explosive data growth demands smarter governance; large models can boost efficiency and precision but raise security and privacy concerns.

3. Deploying multimodal applications at scale. High technical complexity and real‑time interaction requirements make large‑scale rollout a key research topic.

5. Conclusion

The rapid development of large models and inference acceleration is driving AI adoption across research, education, data governance, and many other fields. As AGI gradually materializes, breakthroughs in efficiency, lightweighting, and multimodal agents will support widespread deployment.

Enterprises and developers must closely monitor frontier trends, explore integration points between large models and inference acceleration, and address challenges such as cost‑effectiveness, intelligent data governance, and multimodal deployment to maximize innovation value.

Continued technological innovation and application exploration will soon deliver more intelligent, efficient solutions, propelling artificial intelligence to new heights.