Shanghai AI Laboratory

Shanghai Artificial Intelligence Laboratory (Chinese: 上海人工智能实验室, commonly abbreviated Shanghai AI Lab or SHLAB) is a state-backed, non-profit AI research institution in Shanghai, China, that develops and openly releases frontier models under the "Scholar" or Shusheng (书生) brand, including the InternLM large language model series and the InternVL multimodal models. Unveiled in July 2020 at the World Artificial Intelligence Conference (WAIC), it operates as a "new-type research institution," a category of publicly supported, non-commercial research body in China, and is widely counted among the country's leading AI labs alongside its work on AI safety and large-model evaluation. ^{[1][2][4][29]} In addition to InternLM and InternVL, the lab is known for the InternImage vision backbone and the OpenMMLab and OpenCompass open ecosystems. ^[3][4]

When was the Shanghai AI Laboratory founded?

The lab was inaugurated at WAIC in July 2020 and is based in the Xuhui District of Shanghai; its listed address has moved within the district, from the West Bund International AI Center on Yunjin Road (2023 to 2024) to the International Media Port on Longwen Road (2025) and, as of November 2026, 201 Yunwang Road. ^[2] Its founding director was the computer scientist Tang Xiao'ou (汤晓鸥), who was also a professor at the Chinese University of Hong Kong (CUHK) and a co-founder of SenseTime. ^[5][6] The lab built strategic ties with universities including Shanghai Jiao Tong University, Fudan University, Zhejiang University, and CUHK. ^[2]

The lab describes its mission as conducting strategic, original, and forward-looking research aimed at breakthroughs in fundamental AI theory and core technologies, and at building a large-scale, integrated research base in support of China's AI sector. ^[2] Stated focus areas include scientific multimodal foundation models, embodied AI, AI safety, large-model evaluation, and open data platforms. ^[2] In external profiles the lab is characterized as one of China's leading AI research institutions and as an organization that pairs frontier model development with technical work on AI safety. ^[4] At WAIC 2024, according to MERICS, its director urged a rebalancing of AI investment toward safety research and technical measures, compared AGI to nuclear fusion, and argued that safety is a global public good. ^[4]

What is the Scholar (Shusheng) model family?

The Shusheng (书生) program is the umbrella brand for the lab's foundation models. Its name is usually rendered "Scholar" in English; the language models specifically carry the sub-name Puyu (浦语), giving the full Chinese title 书生·浦语 for InternLM. ^[3][8]

How do InternLM and InternVL work?

InternLM is the lab's flagship large language model line. An initial 104B-parameter research model trained on roughly 1.6T tokens was described in a 2023 technical report. ^[16] The first openly released checkpoint, InternLM-7B, appeared in July 2023, with its weights made available for academic research and free commercial use. ^[8] InternLM-20B followed in September 2023, developed jointly with SenseTime, CUHK, and Fudan University; it used a deeper 60-layer architecture and over 2.3T training tokens. ^[12] The InternLM2 technical report was published in March 2024, reporting gains across six capability dimensions and thirty benchmarks and strong long-context behavior on a 200K-token "needle-in-a-haystack" test. ^[13] InternLM2.5 arrived in July 2024 with native tool-use support, and its 7B-Chat-1M variant extended context to about 1 million tokens. ^[21]

InternLM3-8B-Instruct, released on 15 January 2025, was trained on roughly 4 trillion high-quality tokens. The project reports that this data-centric approach achieves "saving more than 75% of the training cost compared to other LLMs of similar scale," while the model scores 83.0 on MATH-500 and 20.0 on AIME2024 in its deep-thinking mode. ^[22]

InternVL is the multimodal counterpart, developed primarily through the lab's OpenGVLab group. The original InternVL paper, "InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks," scaled the vision foundation model to roughly 6 billion parameters and aligned it with a language model using web-scale image-text data; it was presented as an oral at CVPR 2024. ^[10][11] Follow-up releases such as InternVL-Chat-V1.5 (April 2024) were positioned as open alternatives approaching the performance of contemporary proprietary multimodal systems on benchmarks like MMMU, DocVQA, and MathVista. ^[11] InternImage, an earlier computer vision backbone built on deformable convolutions, was a CVPR 2023 Highlight and integrates with the OpenMMLab detection and segmentation toolkits. ^[9]

What is Intern-S1?

In July 2025 the lab released and open-sourced Intern-S1, described as "the first open-source general-purpose model to integrate advanced scientific capabilities," at the 2025 World Artificial Intelligence Conference. ^[14][23] It uses a Mixture-of-Experts architecture with 241 billion total parameters and about 28 billion activated parameters, built on a Qwen3-235B language core, and was trained on roughly 5T tokens with more than 2.5T drawn from scientific domains. ^[14][26] The model pioneers a "cross-modal scientific reasoning engine" for interpreting data such as molecular formulas, protein sequences, and seismic signals, and the lab reports that on specialized scientific tasks it "outperformed leading closed-source models like Grok-4" while leading open multimodal models such as InternVL3 and Qwen2.5-VL on overall cross-modal capability. ^[14][23] A lightweight 8B variant, Intern-S1-mini, built on Qwen3-8B, followed in August 2025. ^[15][26]

Is the Shanghai AI Laboratory open source?

A defining feature of the lab is the breadth of its open source releases, distributed across GitHub organizations including InternLM, OpenGVLab, OpenMMLab, and OpenCompass, and on the Hugging Face hub. ^[3][4]

OpenMMLab is a family of PyTorch computer vision toolkits whose first components were released in October 2018 by researchers from CUHK and SenseTime, and which is now primarily maintained in association with the lab; Lin Dahua is the project's lead. ^[17] Its best-known toolboxes are MMDetection, an object detection benchmark and toolbox, and MMSegmentation for semantic segmentation, alongside foundational libraries such as MMCV and MMEngine. ^[17][18] OpenGVLab is the group behind the Intern vision and multimodal models, including InternImage and InternVL. ^[9][11]

The lab also maintains a wider toolchain around its models, including the XTuner training and fine-tuning framework, the LMDeploy inference and deployment framework, the MinerU document-parsing tool, and the MindSearch search application. ^[2][3]

In September 2026 the InternLM team released Intern-Decision, three open-weight decision models (0.8B, 2B and 4B parameters) fine-tuned from Qwen3.5 and licensed under Apache 2.0. Instead of generating free text, they answer a set of typed questions about an input with probabilities, using a response format compatible with TypeSafe AI's Jev. ^[24][25]

How does OpenCompass evaluate large models?

OpenCompass is an open-source large language model evaluation platform established by the lab. ^[19] It supports a wide range of open-weight and API models, including Llama 3, Mistral, Qwen, GLM, GPT-4, Claude, and the lab's own InternLM, across more than 100 datasets, and provides leaderboards such as CompassRank. ^[19] OpenCompass 2.0 is organized around three components, CompassKit, CompassHub, and CompassRank, the last of which combines open and proprietary benchmarks; the project publishes a periodic leaderboard covering language, knowledge, reasoning, mathematics, coding, instruction following, and agent capabilities. ^[19] A companion toolkit, VLMEvalKit, extends similar evaluation to large multimodal models. ^[19]

Who leads the Shanghai AI Laboratory?

Tang Xiao'ou (Sean Tang, 1968 to 2023) was the founding director of the lab. He was a professor in the Department of Information Engineering at CUHK, a co-founder of SenseTime, and is widely associated with the development of deep-learning-based face recognition. ^[5][6] He died on the night of 15 December 2023 at the age of 55, after an illness that SenseTime did not disclose, and a memorial service was held in Shanghai on 19 December 2023. ^[5][20]

Following Tang's death, Zhou Bowen (周伯文) became director of the lab, making his public debut in the role at WAIC on 4 July 2024. ^[1][7][27] Zhou previously spent more than a decade at IBM, where he served as chief scientist of the Watson Group and director of the AI Foundations Labs, and later led AI research at the e-commerce company JD.com. ^[7] At WAIC 2024 he set out an "AI-45 degree Law," proposing that AI safety should advance in step with AI capability rather than lag behind it. ^[27][28] Yu Qiao (Qiao Yu), a senior author on the lab's InternVL papers, is among its leading scientists, and Lin Dahua is closely connected to the lab and leads the OpenMMLab project. ^[4][17]

How is the Shanghai AI Laboratory funded?

The lab is described as a state-backed, new-type research institution rather than a commercial company, and is counted among China's "new research and development institutes." ^[4] External analysts characterize state-backed labs like the Shanghai AI Lab as "frontrunners in advanced AI development, and increasingly in governance," and note its role in technical AI safety work and in Chinese standards efforts for large models. ^[4] Its founding, leadership, and major projects are documented in the lab's own materials, the InternLM and InternVL technical reports, and contemporary technology press; precise figures for its budget and headcount are not publicly itemized in the sources cited here, and are therefore not stated. ^[2][4]

The Shanghai AI Laboratory should not be confused with the Beijing Academy of Artificial Intelligence (BAAI), a separate Chinese research institute that produces the BGE text-embedding models; for broader context on China's research landscape see China AI. ^[4]

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• IInternLMInternLM (Chinese name Shusheng Puyu, 书生·浦语) is a family of open-weight large language models developed primarily by Shanghai AI Laboratory, with partners that include SenseTime, the Chinese University of Hong…Mentioned 14 times above
• IInternVLInternVL is a family of open-source multimodal large language models developed by the OpenGVLab research group at the Shanghai Artificial Intelligence Laboratory in collaboration with academic partners…Mentioned 11 times above
• LLanguage ModelA language model is a model of patterns in language that assigns probabilities or comparable scores to linguistic sequences. Its units may be characters, words, subwords, bytes, or other tokens.Mentioned 7 times above
• MMultimodal ModelA multimodal model is a machine learning model, or a model-based system, that processes, relates, or produces information across more than one kind of data. Each kind is called a modality.Mentioned 5 times above
• AAI safetyAI safety is the research and practice of preventing or reducing unacceptable harm from artificial intelligence systems.Mentioned 5 times above
• AArtificial IntelligenceArtificial intelligence (AI) is the field of computer science dedicated to creating systems that perform tasks normally requiring human intelligence, such as learning, reasoning, recognizing patterns…Mentioned 4 times above

More to read

• Model Evaluation
• World Artificial Intelligence Conference
• Frontier Models
• SenseTime
• OpenGVLab
• Jev (AI model)

Topics:Chinese AIResearch Organizations

References

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