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Shanghai Artificial Intelligence LaboratoryProfile date: 2026-08-05 Shanghai Artificial Intelligence Laboratory (Shanghai AI Lab)
Overview and Organizational Profile
The Shanghai Artificial Intelligence Laboratory (also known as Shanghai AI Lab or PJLab) is a high-level national research institution established in Shanghai, China. Positioned at the forefront of global artificial intelligence research and innovation, the laboratory bridges fundamental academic research, strategic national technological development, and industrial application ecosystems.
Operating through strategic partnerships with top academic institutions—such as Shanghai Jiao Tong University, Fudan University, The Chinese University of Hong Kong, and Zhejiang University—alongside major technology enterprise partners like SenseTime, Shanghai AI Lab functions as an open research facility. Its overarching mission is to advance the frontiers of Artificial General Intelligence (AGI), develop foundational open-source technology infrastructures, drive AI for Science (AI4S), and foster safe, controllable, and human-centric AI ecosystems.
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Core Mission and Strategic Focus Areas
Shanghai AI Lab focuses on foundational breakthroughs across several core computing paradigms:
- Foundational Large Language and Multimodal Models: Developing state-of-the-art open-weight foundation models spanning text, vision, audio, and video capabilities.
- AI for Science (AI4S): Accelerating scientific discovery in biology, materials science, chemistry, physics, and meteorology using high-parameter AI architectures.
- Open-Source Software Infrastructures: Building comprehensive toolchains for model pre-training, fine-tuning, quantization, deployment, and multi-agent systems.
- Autonomous Driving and Embodied Intelligence: Researching vision-centric and spatial-aware reasoning models for autonomous vehicles and robotics.
- AI Safety and Governance: Creating frameworks for alignment, evaluation, and safe deployment of generative models and AI agents.
Key Products, Foundation Models, and Platforms
Shanghai AI Lab provides an extensive suite of models, open-source software systems, and cloud platforms for developers, researchers, and industrial enterprises.
1. The Intern Model Series (书生大模型)
The lab’s flagship contribution to global open-source AI is the Intern series (Intern-series large models), which includes foundational language, multimodal, and domain-specialized models:
- InternLM Series (InternLM, InternLM2, InternLM2.5, InternLM3): Multi-lingual foundation and chat models trained on multi-trillion token datasets. Known for high performance in complex reasoning, mathematics, coding, and long-context handling.
- InternVL: Advanced multimodal large language model (MLLM) family designed to bridge vision and text understanding. InternVL models rival closed-source proprietary systems on vision-language benchmarks.
- Intern-S1 / Intern-S1-Pro: Ultra-large Mixture-of-Experts (MoE) scientific multimodal foundation models designed specifically for scientific tasks, utilizing innovative position embeddings (FoPE) to interpret signals ranging from micro-scale biology to macro-scale astronomical phenomena.
- InternLM-XComposer: Advanced vision-language models capable of contextual text-image comprehension, interleaved generation, and long-term streaming visual-audio interaction.
- InternLM-Math: Bilingual mathematical reasoning large language models tuned for theorem proving, problem solving, and step-by-step mathematical reasoning.
2. Full-Stack Development Toolchain and Engines
To facilitate end-to-end model creation, optimization, and deployment, the laboratory maintains a comprehensive stack of software projects:
- LMDeploy: An ultra-efficient toolkit designed for compressing, quantizing, deploying, and serving LLMs and vision-language models at scale.
- XTuner: A lightweight, high-performance training engine built specifically for fine-tuning ultra-large Mixture-of-Experts (MoE) and dense foundation models.
- Lagent: An agent development framework that enables LLMs to perform multi-step planning, tool selection, API calling, and task execution.
- MindSearch: An LLM-driven multi-agent search engine framework designed to deliver deep web search and information retrieval capabilities similar to advanced research assistants.
- OpenMMLab: The world’s leading open-source computer vision algorithm platform, covering detection (MMDetection), segmentation (MMSegmentation), pose estimation (MMPose), action recognition, 3D perception, and generative vision models.
3. OpenGVLab and Computer Vision Projects
Developed by the General Vision group at Shanghai AI Lab, OpenGVLab provides foundational perception frameworks:
- InternVideo: Video foundation models tailored for fine-grained video action recognition, temporal understanding, and long-form video QA.
- InternImage: Scalable vision foundation models utilizing deformable convolutions for high-resolution object detection, segmentation, and perception tasks.
- VideoChat & VideoChat-Flash: Video-centric interactive models supporting long-context video comprehension and real-time conversation over video streams.
4. Autonomous Driving Research (ADLab & OpenDriveLab)
Shanghai AI Lab operates specialized teams dedicated to real-world embodiment and mobility:
- ADLab Systems: Knowledge-driven autonomous driving architecture integrating common-sense reasoning and trajectory prediction to enhance vehicle safety and reliability.
- UniAD / Spatial AI: Vision-centric end-to-end driving models that unify perception, prediction, and planning into a single multi-task neural network.
5. AI for Science (AI4S) Initiatives
- FengWu Weather Model: AI-driven global meteorological forecasting system that outperforms traditional numerical weather prediction (NWP) systems in medium-range atmospheric predictions.
- Biomedical & Materials AI: Foundation models optimized for protein structure prediction, drug molecule design, and material property synthesis.
6. Cloud Services and Community Platforms
- OpenXLab: An open-access platform providing compute access, dataset repositories, model hosting, and interactive demo deployments for research communities.
- InternStudio: A cloud development environment offering pre-configured software stacks and GPU compute allocations for AI developers and academics.
- Chat Intern: An interactive web service (chat.intern-ai.org.cn) demonstrating the capabilities of the InternLM and InternVL foundational models.
Technical Infrastructure and Academic Collaboration
Shanghai AI Lab maintains extensive high-performance GPU supercomputing infrastructure, enabling full-scale pre-training of trillion-parameter MoE architectures. The laboratory actively hosts Special Interest Groups (SIGs) covering key research vectors including Retrieval-Augmented Generation (RAG), Fine-Tuning/Roleplay, Agentic Workflows, Multimodal Synthesis, and Model Evaluation.
Through its open-source philosophy, research publications at top venue conferences (CVPR, NeurIPS, ICLR, ICML, ECCV), and community tooling, Shanghai AI Lab operates as an essential global hub for open-weight generative AI and foundational research.
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