DeepSeek has announced its software development roadmap for the inference engine, following their use of a custom version of vLLM to enhance internal operations in collaboration with open-source projects.
Previously, DeepSeek released software related to artificial intelligence systems in abundance, often optimizing the efficiency of running AI models on NVIDIA Hopper chips. DeepSeek had considered releasing the engine as a whole, but encountered three main issues:
1. The vLLM code from DeepSeek has diverged significantly from the main project, making it challenging to integrate into other workflows.
2. The engine is intricately tied with DeepSeek’s underlying infrastructure, including internal cluster management systems, complicating its usage elsewhere.
3. The team lacks sufficient resources to be the primary caretakers of a large-scale open-source project.
Moving forward, DeepSeek will collaborate with the original open-source project (potentially vLLM, though not confirmed) by providing specialized feature codes for implementation. Additionally, they will assist in optimizing the AI inference engine for higher efficiency.
This approach will enable the open-source AI engine to effectively run the latest DeepSeek models from the get-go, simplifying external usage and accessibility for end-users.
Source: GitHub: DeepSeek-AI
TLDR: DeepSeek announces collaboration with open-source projects to enhance AI inference engine efficiency and facilitate easier external model implementation.
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