DeepSeek Open-Sources Custom Inference Engine Built on vLLM

DeepSeek has open-sourced its proprietary inference engine, furthering its commitment to transparency and innovation in the AI community. The move follows the company’s successful Open Source Week, during which it released key tools and models, and aims to accelerate deployment for advanced systems like DeepSeek-V3 and DeepSeek-R1.

Built on PyTorch and vLLM

DeepSeek’s training framework is powered by PyTorch, which enables efficient large-scale model training with flexible tensor operations and distributed computing. For inference, the company built on vLLM, leveraging its optimized memory management and fast tokenizer execution to significantly boost the speed and scalability of model deployment.

This architecture has supported the development of DeepSeek’s high-performance language models, streamlining both training and inference processes.

Open-Sourcing Challenges

Despite its benefits, releasing the engine presented notable hurdles:

• Codebase Divergence: The engine originated from an early fork of vLLM, significantly customized for DeepSeek’s specific model needs. This customization limits its general usability for broader applications.
  

• Infrastructure Lock-in: The engine is tightly coupled with DeepSeek’s internal infrastructure, including proprietary cluster management tools. These dependencies make public deployment challenging without substantial rework.
  

• Limited Maintenance Capacity: As a lean research team focused on advancing model development, we currently lack the resources to actively maintain a large-scale open-source project.

A Sustainable Path Forward

In light of the challenges tied to open-sourcing its heavily customized inference engine, DeepSeek has opted to collaborate with existing open-source projects rather than maintain a standalone framework. The company framed this approach as a more sustainable and community-aligned path forward.

DeepSeek’s future contributions will focus on two key areas:

• Extracting Standalone Features: Modularizing internal components and releasing them as independent, reusable libraries.
  

• Sharing Optimizations: Upstreaming design improvements and implementation refinements to enhance the performance and usability of broader open-source tools.

By integrating with established ecosystems, DeepSeek aims to maximize its impact while reducing the overhead of maintaining a parallel infrastructure.

A Step Toward Open AGI

DeepSeek emphasized that this release is part of its broader vision to support the open-source ecosystem and contribute meaningfully to the progress of artificial general intelligence (AGI). By sharing internal tools—even with limitations—the company hopes to foster collaboration and transparency in the AI research community.

Scope and Future Collaboration Plans

DeepSeek clarified that this announcement pertains specifically to the open-sourcing of its DeepSeek Inference Engine codebase. The company emphasized that its broader commitment to openness extends beyond infrastructure, highlighting plans for future collaboration with both the open-source community and hardware partners.

To support this, DeepSeek intends to synchronize its inference engineering efforts ahead of upcoming model releases. The aim is to ensure Day-0 support for state-of-the-art performance across diverse hardware platforms.

This forward-looking approach signals DeepSeek’s ambition to build a tightly coordinated AI ecosystem—where cutting-edge capabilities are accessible and deployable the moment new models become available.

What This Means

DeepSeek’s decision to release its inference engine, despite technical barriers, signals a strong cultural shift toward openness in AI infrastructure. While it may not be plug-and-play for every developer, it offers a valuable look into how a leading AI lab builds and runs its models at scale.

In a global landscape where the U.S. and China are racing to lead in AI, such open contributions reflect a broader strategic emphasis—not just on innovation, but on shaping ecosystems. By sharing internal tools, DeepSeek reinforces China’s growing presence in foundational AI infrastructure and global research collaboration.

In the long run, it’s this spirit of open collaboration—not the race to be first—that may shape the most enduring breakthroughs in AI.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.