The DeepSeek project has announced the open-source release of DeepEP, a library designed for training and inference using Mixture of Experts (MoE) models. This release is expected to introduce various enhancements to MoE methodologies. Key features include: 1. Efficient all-to-all communication that enhances performance. 2. Support for both intranode and internode communication using advanced technologies like NVLink and RDMA. 3. High-throughput kernels that speed up both training and inference. 4. Low-latency kernels for quick inference decoding. 5. Native support for FP8 dispatch, allowing for efficient computation with lower precision. 6. Flexible control over GPU resources to facilitate overlapping computation and communication. There is a sense of anticipation among the community that these advancements may illustrate a resurgence in the use of MoE models in academia. Users have expressed excitement over the potential impact of these innovative features on their research and modeling efforts, as demonstrated by appreciation for the open-source efforts. Users are also inquiring about PTX instructions and their inclusion in the release, indicating the community's eagerness for detailed technical information to utilize the library effectively.