DeepSeek, a Chinese AI startup, is making waves in the AI community by open-sourcing key components of its AI inference engine. This move aims to foster collaboration, accelerate innovation, and democratize access to advanced AI technologies. While not fully open-sourcing the entire engine, DeepSeek is sharing valuable technical details and improvements made to the vLLM inference engine, a popular open-source library for Large Language Model (LLM) inferencing developed by researchers at UC Berkeley.

The decision to open-source parts of its inference engine stems from DeepSeek's deep appreciation for the open-source ecosystem, which it credits as being instrumental in its progress towards Artificial General Intelligence (AGI). The company's training framework relies on PyTorch, while its inference engine is built upon vLLM. Recognizing the increasing demand for deploying models like DeepSeek-V3 and DeepSeek-R1, DeepSeek aims to "give back to the community as much as we can."

Instead of a complete open-source release, DeepSeek will contribute design improvements and implementation details to existing open-source projects. The company will also extract useful features and share them as standalone, reusable libraries. This approach allows DeepSeek to contribute to the community while addressing challenges associated with fully open-sourcing its internal inference engine.

DeepSeek identified several obstacles to a complete open-source release, including:

• Codebase Divergence: The inference engine is based on an early fork of vLLM from over a year ago, heavily customized for DeepSeek models, making it difficult to extend for broader use cases.
• Infrastructure Dependencies: The engine is tightly coupled with DeepSeek's internal infrastructure, including cluster management tools, making it impractical for public deployment without significant modifications.
• Limited Maintenance Bandwidth: As a research team focused on developing better models, DeepSeek lacks the bandwidth to maintain a large open-source project.

Despite these challenges, DeepSeek is committed to proactively synchronizing inference-related engineering efforts before new model launches. The goal is to enable the community to achieve state-of-the-art support from day zero. DeepSeek aims to foster a synchronized ecosystem where cutting-edge AI capabilities can be seamlessly implemented across diverse hardware platforms upon official model releases.

DeepSeek's move has been celebrated by AI researchers and tech executives for its potential to promote wider adoption and innovation in the AI field. By sharing its expertise and resources, DeepSeek hopes to encourage a more collaborative and accessible AI landscape. The company's open-source strategy has garnered international attention, influencing the AI landscape and promoting greater transparency in the development and deployment of AI technologies.

This initiative builds upon DeepSeek's previous open-source efforts, including the release of portions of its AI models and code repositories during its "open-source week" initiative earlier this year. These actions underscore DeepSeek's commitment to open collaboration and its belief in the power of the open-source community to drive AI innovation.

DeepSeek's decision to open-source parts of its inference engine is a significant step towards democratizing AI and fostering a more collaborative ecosystem. By sharing its expertise and resources, DeepSeek is contributing to the advancement of AI technology and ensuring that its benefits are more widely accessible. As DeepSeek continues its journey toward greater openness, it will undoubtedly play a key role in shaping the future of AI.