DeepSeek open source DeepEP – library for MoE training and Inference (github.com)

Article summary

DeepSeek has released DeepEP, a high-performance communication library for machine learning training and inference. The library focuses on expert parallelism and provides high-throughput and low-latency all-to-all GPU kernels with low-precision support. DeepEP is designed for zero or minimal SM occupation and achieves extreme performance with several times fewer SM resources compared to its previous version. The library is open-sourced and available on GitHub.

Main themes

• Deep Learning
• Machine Learning Libraries
• Parallel Computing
• GPU Optimization
• Open Source Software

What commenters say

• DeepSeek's open-sourcing of DeepEP is a significant contribution to the AI community, promoting transparency and collaboration.
• OpenAI's lack of openness and transparency is a major concern, and their actions are driven by profit rather than a desire to benefit humanity.
• The use of PTX instructions in DeepEP's code is a key factor in its high performance and low training and inference costs.
• The release of DeepEP may mark a shift in the AI landscape, with open-source models and libraries potentially surpassing proprietary ones in terms of performance and adoption.
• The distinction between 'open' and 'open-source' is crucial, and some companies may use the term 'open' to imply a level of transparency and collaboration that they do not actually provide.
• The development of MoE models is an important area of research, and the release of open-source models and libraries like DeepEP can accelerate progress in this field.
• The trade-offs between computation and memory in MoE models can make them harder to set up in small labs, which may hinder their adoption.
• The true intentions and motivations of companies like OpenAI and DeepSeek are subject to interpretation and debate, with some viewing them as driven by a desire for profit and others seeing them as genuinely committed to advancing AI research.