Shanghai AI Laboratory
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Last reviewed
Jun 3, 2026
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20 citations
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Source-backed
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v1 · 1,469 words
Add missing citations, update stale details, or suggest a clearer explanation.
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 research institution in Shanghai, China, that works on artificial intelligence. It was unveiled in July 2020 at the World Artificial Intelligence Conference (WAIC) and operates as a "new-type research institution," a category of publicly supported, non-profit research body in China. [1][2] The lab is best known internationally for a large body of open-source work released under the "Scholar" or Shusheng (书生) brand, including the InternLM large language model series, the InternVL multimodal models, the InternImage vision backbone, and open evaluation and toolkit ecosystems such as OpenMMLab and OpenCompass. [3][4]
The lab was inaugurated at WAIC in July 2020 and is based in the Xuhui District of Shanghai, at the West Bund media cluster on Longjing Road. [1][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 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; at WAIC 2024 its leadership publicly argued for rebalancing AI investment toward safety research. [4][7]
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]
| Project | First release | Notes |
|---|---|---|
| InternImage | 2022 (CVPR 2023 Highlight) | Vision backbone using deformable convolutions; released under OpenGVLab. [9] |
| InternVL | December 2023 (CVPR 2024 Oral) | Vision-language model scaling the vision encoder to about 6B parameters. [10][11] |
| InternLM-7B | July 2023 | First open InternLM language model; weights open for research and free commercial use. [8] |
| InternLM-20B | September 2023 | 20B model, 60 layers, trained on over 2.3T tokens. [12] |
| InternLM2 | March 2024 | 6-dimension, 30-benchmark evaluation; long-context to 200K tokens. [13] |
| Intern-S1 | July 2025 | Scientific multimodal MoE model, 241B total / 28B active parameters. [14][15] |
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] Later iterations include InternLM2.5 and InternLM3, the latter reported as trained on about 4T high-quality tokens to cut training cost relative to similarly sized models. [3]
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]
In July 2025 the lab released Intern-S1, a scientific multimodal foundation model. 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][15] A lightweight 8B variant, Intern-S1-mini, built on Qwen3-8B, followed in September 2025. [15]
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. [3]
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]
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 5 July 2024. [7] 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] Yu Qiao (Qiao Yu) is identified as a leading scientist at the lab, associated with its open-source image and video generation work, and Lin Dahua is closely connected to the lab and leads the OpenMMLab project. [4][17]
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." [1][7] External analysts place it among the country's top AI research organizations 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]