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Large Model Research Center, School of Computer Science, Nanjing University

The Large Model Center conducts innovative research on large model system architectures, learning algorithms, and domain applications, providing core technologies for the next generation of artificial intelligence. The main research directions include scalable system architectures for large models, high-performance machine learning algorithms and platforms for large models, large model knowledge-enhanced learning algorithms, as well as language large models, multimodal large models, scientific large models, embodied decision-making large models, intelligent agent systems, and neural-symbolic reasoning systems, etc.

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ICCV 2025 Accepted Papers

ICCV is one of the most influential top-tier conferences in computer vision. It is organized by the IEEE Computer Society and held biennially alongside CVPR and ECCV as the three flagship vision venues. ICCV covers cutting-edge topics such as image processing, object detection, 3D reconstruction, video understanding, and vision–language research, serving as a premier platform for presenting the latest advances and exchanging ideas. With its very high acceptance standards, ICCV represents the frontier trends and research hotspots of the field. Seven papers from the Large Model Center of the Department of Computer Science and Technology, Nanjing University, have been accepted to ICCV 2025.

ICML 2025 Accepted Papers

ICML is one of the most prestigious and influential conferences in machine learning. It is among the longest-running and largest venues in the field and a CCF Class-A conference. Four papers from the Large Model Center of the Department of Computer Science and Technology, Nanjing University, were accepted to ICML 2025.

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Research Groups

Large Language Model Research Group
Multimodal Large Model Research Group
Embodied Decision Large Model Research Group
Large Model Knowledge Enhancement Research Group
Large Model Learning Algorithms and Platform Research Group
Large Model Systems and Platforms Research Group

Large Model Systems and Platforms Research Group

## Large Model Systems and Platforms: The Core Engine Driving the Scalable Application of Artificial Intelligence With the rapid development of large model technology, efficiently training, deploying, and managing these massive models has become a critical challenge. **Large model systems and platforms** have emerged to address this need, providing the infrastructure and toolchains necessary for the development and application of large-scale artificial intelligence models. They serve as the core engine driving the scalable application of AI. ### Core Features and Capabilities Large model systems and platforms typically offer the following core functionalities: 1. **Distributed Training**: - Supports distributed training for massive datasets and ultra-large models. - Provides efficient parallel computing and communication optimization, such as data parallelism, model parallelism, and pipeline parallelism. - Representative examples: Megatron-LM, DeepSpeed. 2. **Efficient Inference**: - Optimizes inference for large models to reduce latency and resource consumption. - Supports model compression, quantization, and acceleration techniques. - Representative examples: TensorRT, ONNX Runtime. 3. **Model Management and Deployment**: - Offers version control, monitoring, and updating capabilities for models. - Supports deployment across multiple environments, including cloud, edge, and devices. - Representative examples: MLflow, Kubeflow. 4. **Developer Tools and Ecosystem**: - Provides user-friendly APIs, SDKs, and visualization tools. - Builds open developer communities and ecosystems. - Representative examples: Hugging Face, OpenAI API. ### Representative Platforms and Systems The following are some notable large model systems and platforms: - **Hugging Face**: Offers a rich collection of pre-trained models and datasets, supporting model training, fine-tuning, and deployment. - **OpenAI API**: Provides powerful interfaces for large model services, enabling tasks like text generation and code generation. - **DeepSpeed**: Developed by Microsoft, focuses on distributed training and optimization for large-scale models. - **Colossal-AI**: Delivers efficient solutions for parallel training and inference, supporting ultra-large models. ### Future Development Trends The future development of large model systems and platforms will focus on the following directions: 1. **Performance Optimization**: Further improves training and inference efficiency while reducing resource consumption. 2. **Usability Enhancement**: Simplifies development processes and lowers the barrier to entry. 3. **Ecosystem Expansion**: Builds a more open and thriving developer ecosystem. 4. **Security and Trustworthiness**: Strengthens model security and explainability to ensure reliable applications. --- **In summary, large model systems and platforms are the critical enablers for the practical application of large model technology.** With continuous technological advancements and ecosystem improvements, they will provide stronger momentum for the scalable application of artificial intelligence, driving intelligent transformation across industries.

Scientific Large Model Research Group
Medical Imaging Large Model Research Group
Edge Large Model System Research Group
Large Language Model System Research Group
Cloud Large Model System Research Group

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