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+# The Path to Open-Sourcing the DeepSeek Inference Engine (DeepSeek-vLLM)
+
+A few weeks ago,
+during [Open Source Week](https://github.com/deepseek-ai/open-infra-index?tab=readme-ov-file#202502-open-source-week),
+we open-sourced several libraries.
+The response from the community has been incredibly positive - sparking inspiring collaborations, productive
+discussions, and valuable bug fixes.
+Encouraged by this, we’ve decided to take another step forward: contributing our internal inference engine back to the
+open-source community.
+
+We are deeply grateful for the open-source ecosystem, without which our progress toward AGI would not be possible.
+Our training framework relies on [PyTorch](https://github.com/pytorch/pytorch), and our inference engine is built
+upon [vLLM](https://github.com/pytorch/pytorch),
+both of which have ben instrumental in accelerating the training and deployment of DeepSeek models.
+
+Given the growing demand for deploying models like [DeepSeek-V3](https://github.com/deepseek-ai/DeepSeek-V3)
+and [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1), we want to give back to the community as much as we can.
+While we initially considered open-sourcing our full internal inference engine, we identified several challenges:
+
+- **Codebase Divergence**: Our engine is based on an early fork of vLLM from over a year ago. Although structurally
+  similar, we’ve heavily customized it for DeepSeek models, making it difficult to extend for broader use cases.
+- **Infrastructure Dependencies**: The engine is tightly coupled with our internal infrastructure, including cluster
+  management tools, making it impractical for public deployment without significant modifications.
+- **Limited Maintenance Bandwidth**: As a small research team focused on developing better models, we lack bandwidth to
+  maintain a large open-source project.
+
+Considering these challenges, we’ve decided to collaborate with the vLLM project as a more sustainable alternative.
+vLLM stands out for its broad hardware support, extensive model compatibility, and state-of-the-art performance.
+Its modular architecture gives us confidence that we can adapt and contribute key components from our internal engine to
+benefit the wider community.
+
+Moving forward, we will work closely with the vLLM team to:
+
+- **Extract Standalone Features**: Modularize and contribute reusable components as independent libraries.
+- **Share Optimizations**: Contribute design improvements and implementation details directly to vLLM.
+
+We are profoundly grateful for the open-source movement - from operating systems and programming languages to machine
+learning frameworks and inference engines. It’s an honor to contribute to this thriving ecosystem and to see our models
+and code embraced by the community. Together, let’s push the boundaries of AGI and ensure its benefits serve all of
+humanity.
+
+This page will serve as a central hub for tracking our related open-source efforts on the inference engine.
+
+> [!NOTE]
+> **To clarify, this article outlines our approach to open-sourcing of our DeepSeek-vLLM codebase only.
+> Regarding future model releases, we maintain an open and collaborative stance towards both the open-source community
+> and hardware partners.
+> We commit to proactively synchronizing inference-related engineering efforts prior to new model launches, with the
+> goal of enabling the community to achieve state-of-the-art (SOTA) support from Day-0. Our ultimate aim is to foster a
+> synchronized ecosystem where cutting-edge AI capabilities can be seamlessly implemented across diverse hardware
+> platforms upon official model releases.**
diff --git a/README.md b/README.md
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 # Hello, DeepSeek Open Infra!
 
+## 202504 [The Path to Open-Sourcing the DeepSeek Inference Engine (DeepSeek-vLLM))](OpenSourcing_DeepSeek-vLLM/README.md)
+
 ## 202502 Open-Source Week
 We're a tiny team @deepseek-ai pushing our limits in AGI exploration.