DeepEP

DeepEP is a high-performance communication library for Mixture-of-Experts (MoE) and expert parallelism (EP), providing throughput‑oriented and latency‑optimized GPU all‑to‑all (dispatch / combine) primitives with optional FP8 / BF16 support.

This edition adds preliminary AMD GPU (ROCm) enablement:

• xGMI / Infinity Fabric intranode + InfiniBand / RoCE RDMA internode with HIPIFY kernels.
• Unified Python API with the upstream deepseek-ai/DeepEP
• Some NVIDIA-only optimizations (e.g., PTX loads) are not mirrored; AMD path focuses on correctness + baseline overlap.

Notice:

• Implementation details may differ from the DeepSeek-V3 paper, and AMD behavior may lag NVIDIA in advanced tuning until subsequent releases.
• The Pytorch commit e4adf5d fixed a bug of the -fgpu-rdc flag in Pytorch ROCm submodule. Please use any Pytorch version later than this commit, e.g., nightly Pytorch. (If one is using a Pytorch version that is older than e4adf5d, please apply the patch for Pytorch in rocSHMEM Installation Guide)

Quick start

Requirements

• MI308X/MI300X GPU (will support more architectures or devices later)
• Python 3.8 and above
• ROCm 6.3.4
• PyTorch e4adf5d and above
• xGMI for intranode communication
• RDMA network for internode communication

Download and install rocSHMEM dependency

DeepEP (AMD version) depends on rocSHMEM. Please take a look at rocSHMEM Installation Guide for instructions.

Development

git clone https://github.com/ROCm/DeepEP
cd DeepEP

# export OMPI dir in the next command (e.g., it's $BUILD_DIR/ompi in third-party/README.md)
export OMPI_DIR=<ompi_dir>
python3 setup.py --variant rocm build develop

# Run test cases
# NOTES: you may modify the `init_dist` function in `tests/utils.py`
# according to your own cluster settings, and launch into multiple nodes
python3 tests/test_intranode.py
python3 tests/test_internode.py
python3 tests/test_low_latency.py

Installation

python3 setup.py --variant rocm build develop

Then, import deep_ep in your Python project, and enjoy!

Network configurations

Currently, DeepEP (AMD version) supports Mellanox ConnectX-7 NIC (will support more NICs later).

Interfaces and examples

DeepEP (AMD version) shares the same Python interface with deepseek-ai/DeepEP; please refer to the example in the upstream README

Roadmap

• MI350X support
• AMD Pensando AI NIC support
• Broadcom NIC support

License

This code repository is released under the MIT License.

Citation

The credits of the original innovation belong to deepseek-ai/DeepEP; please cite their paper:

@misc{deepep2025,
      title={DeepEP: an efficient expert-parallel communication library},
      author={Chenggang Zhao and Shangyan Zhou and Liyue Zhang and Chengqi Deng and Zhean Xu and Yuxuan Liu and Kuai Yu and Jiashi Li and Liang Zhao},
      year={2025},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/deepseek-ai/DeepEP}},
}