Chinese AI startup DeepSeek is taking a significant step toward openness by sharing key technical insights into one of the core components of its AI infrastructure—its internal inference engine. The move is aimed at enhancing collaboration and accelerating progress in the global AI development ecosystem.
Inference, a crucial stage in large language model (LLM) deployment, involves the generation of new data by a trained model, revealing learned patterns and associations. DeepSeek’s proprietary inference engine, built atop a modified version of vLLM—an open-source library developed by UC Berkeley researchers—has been instrumental in optimizing both the training and deployment of its AI models.
While the startup is not releasing its inference engine as fully open-source, it will be sharing design improvements, implementation details, and standalone reusable libraries with the broader community. This includes contributions to existing open-source projects, enabling other developers to benefit from DeepSeek’s performance enhancements without needing full access to the proprietary codebase.
“We’re grateful for the open-source ecosystem that has fueled our progress toward AGI,” said a DeepSeek researcher in a post on Hugging Face, an open-source AI model platform. “In return, we aim to give back wherever possible.”
DeepSeek cited several challenges to fully open-sourcing its inference engine, including infrastructure limitations, a customized codebase, and a lack of maintenance resources. Nonetheless, this initiative reflects the company’s ongoing commitment to fostering transparency and community engagement.
Earlier this year, DeepSeek launched its “open-source week” by releasing parts of its code and repositories. However, industry observers have noted that DeepSeek’s models do not meet the Open Source Initiative (OSI) standards for fully open-source AI, as key elements such as training data and code are not licensed under the permissive MIT license.
