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DeepSeek Open Source Week Day 2: DeepEP Optimizes MoE Model Training

DeepSeek Open Source Week Day 2: DeepEP Optimizes MoE Model Training

Alicia Shapiro
February 25, 2025 • Estimated Reading Time: 6 minutes

A futuristic AI training center with multiple GPUs interconnected through high-speed data links. The image highlights DeepEP’s role in optimizing Mixture-of-Experts (MoE) models, featuring glowing neural network pathways connecting different expert models. The scene conveys the efficiency and scalability of DeepEP’s communication framework.

Image Source: ChatGPT-4o

DeepSeek Open Source Week Day 2: DeepEP Optimizes MoE Model Training

On Day 2 of its Open Source Week, DeepSeek unveiled DeepEP, the first open-source Expert Parallelism (EP) communication library designed to optimize Mixture-of-Experts (MoE) model training and inference. DeepEP introduces a suite of high-performance GPU communication features, including high-throughput all-to-all operations, low-latency inference kernels, and native FP8 dispatch support, making it a significant advancement for large-scale AI training.

A screenshot of DeepSeek’s official X (Twitter) post announcing DeepEP on Day 2 of Open Source Week. The post highlights DeepEP as the first open-source Expert Parallelism (EP) communication library for Mixture-of-Experts (MoE) model training and inference. It lists key features, including optimized all-to-all communication, NVLink and RDMA support, high-throughput training kernels, low-latency inference kernels, native FP8 dispatch, and flexible GPU resource control. The post includes a GitHub link to DeepEP’s repository and has received over 885.9K views, 6.8K likes, and 1.1K reposts.

Image Source: DeepSeek X Post

Optimizing MoE Models with Expert Parallelism

DeepEP enhances the efficiency of MoE models, which distribute computation across specialized expert networks to improve scalability. The library provides:

High-throughput, low-latency GPU communication for efficient MoE dispatching and combining.
Support for both intra-node (NVLink) and inter-node (RDMA) communication, optimizing data transfer between GPUs.
Low-precision operations, including FP8 dispatching and BF16 combining, reducing computational costs.
Flexible GPU resource management, allowing computation-communication overlap to maximize performance.

The library is aligned with the group-limited gating algorithm from DeepSeek-V3 and introduces optimized asymmetric-domain bandwidth forwarding, further improving inference and training performance.

DeepEP Performance Benchmarks

DeepSeek tested DeepEP on NVIDIA H800 GPUs with NVLink and CX7 InfiniBand 400 Gb/s RDMA networking, following the DeepSeek-V3/R1 pretraining settings. The results show:

High-Throughput Communication Performance

Intra-node (NVLink) bottleneck bandwidth: ~153–158 GB/s
Inter-node (RDMA) performance: Scales from 43 GB/s (16 experts) to 46 GB/s (64 experts)

Low-Latency Inference Decoding

Latency for dispatching and combining scales from 163µs (8 experts) to 369µs (128 experts).
RDMA bandwidth remains stable (~39–46 GB/s) across different expert configurations.

These benchmarks highlight DeepEP’s ability to optimize both large-scale training and real-time inference, making it a valuable tool for AI researchers working with MoE architectures.

A performance benchmark table showing DeepEP’s bottleneck bandwidth for Mixture-of-Experts (MoE) model training and inference. The table compares intra-node (NVLink) and inter-node (RDMA) communication across different expert parallelism (EP) configurations. Results indicate that NVLink achieves up to 158 GB/s bandwidth, while RDMA scales from 43 GB/s to 46 GB/s depending on the number of experts. This benchmark highlights DeepEP’s efficiency in expert dispatch and combination for large-scale AI models.

DeepEP Performance: High-Throughput GPU Communication for MoE Models. Image Source: GitHub

A table presenting DeepEP’s low-latency performance for MoE model inference using RDMA communication. The results show latency values ranging from 163µs (8 experts) to 369µs (128 experts) for dispatch operations and from 318µs to 360µs for combination operations. RDMA bandwidth remains stable between 39 GB/s and 46 GB/s across different expert configurations. These results demonstrate DeepEP’s ability to reduce inference delays while maintaining high bandwidth efficiency.

DeepEP Performance: Low-Latency Inference with RDMA Optimization. Image Source: GitHub

Why DeepEP Matters

The release of DeepEP marks a significant step in the open-source AI landscape, as it provides a publicly available, high-performance library for improving MoE model training. Traditionally, efficient MoE scaling has been a challenge due to communication bottlenecks, but DeepEP helps mitigate these issues by offering a specialized, optimized framework.

This development also reflects a growing industry trend toward open-source AI infrastructure, as companies like DeepSeek push the boundaries of large-scale model training.

You can read the full DeepSeek-V3 paper here.

Looking Ahead

With the introduction of DeepEP, DeepSeek continues to expand its contributions to the open-source AI community, providing developers with cutting-edge tools for MoE model optimization. As AI models grow increasingly complex, innovations like DeepEP will play a crucial role in enabling scalable, high-performance machine learning systems. Check back tomorrow for what’s in store for Day 3.

Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.