DeepSeek's DeepEP: Revolutionizing Multi-GPU Training and Inference for Mixture-of-Experts Models

Overview

DeepEP, developed by DeepSeek AI , appears to be a communication library designed to enhance the performance of Mixture-of-Experts (MoE) models in multi-GPU environments. MoE models are neural networks that use multiple specialized sub-networks (experts) to handle different input data, allowing for larger model capacity without a proportional increase in computational cost. DeepEP seems to address the communication challenges in these models, making it a valuable tool for researchers and developers.

Features and Functionality

DeepEP likely offers high-throughput kernels for training and inference prefilling, optimized for asymmetric-domain bandwidth forwarding, such as from NVLink to RDMA. It also seems to include low-latency kernels for latency-sensitive inference decoding using pure RDMA, supporting Streaming Multiprocessor (SM) number control and a hook-based communication-computation overlapping method that doesn't occupy SM resources. These features suggest it is well-suited for both training and real-time inference tasks.

Alignment with DeepSeek-V3

The evidence leans toward DeepEP aligning with the group-limited gating algorithm from DeepSeek-V3, a recent MoE language model with 671B total parameters. This alignment likely ensures efficient load balancing in MoE models without the need for an auxiliary loss, which is an unexpected detail given traditional MoE approaches often rely on such losses.

For more detailed information, you can explore the GitHub repository at this link.

Comprehensive Analysis

DeepEP, launched by DeepSeek AI as part of their Open Source Week on February 25, 2025, represents a significant advancement in the field of multi-GPU training and inference for Mixture-of-Experts (MoE) models. This section provides a detailed examination of DeepEP, its features, performance metrics, and its integration with DeepSeek's broader technological ecosystem, particularly the DeepSeek-V3 model.

Background and Context

DeepSeek, a Chinese AI company founded in July 2023 and based in Hangzhou, Zhejiang, has been making waves in the AI community with its cost-effective and high-performance large language models (LLMs). The company, owned and funded by the hedge fund High-Flyer, has released several models, including DeepSeek-V3, which is a Mixture-of-Experts (MoE) language model with 671B total parameters, of which 37B are activated for each token. MoE models are a type of neural network architecture that employs multiple specialized sub-networks, or "experts," to handle different parts of the input data. This approach allows for scaling up model capacity without a proportional increase in computational cost, as only a subset of experts is activated for each input, enhancing efficiency.

However, the distributed nature of MoE models, especially in multi-GPU settings, introduces significant communication challenges. Efficiently exchanging data among devices is critical, as traditional all-to-all communication methods can create bottlenecks, increasing latency and underutilizing GPU resources. This is particularly problematic in latency-sensitive settings, such as real-time inference, where even small delays can impact performance. Additionally, low-precision operations, such as FP8, which help reduce memory usage, require careful optimization to maintain model quality.

DeepEP: An Overview

DeepEP is an open-source communication library specifically designed to address these challenges, focusing on expert parallelism (EP) within MoE models. It was released on February 25, 2025, as part of DeepSeek's initiative to open-source five repositories during their Open Source Week, highlighting their commitment to community-driven AI development. The library aims to provide high-throughput and low-latency many-to-many GPU kernel communication, commonly known as MoE dispatch and combine, which are essential for routing data between experts and aggregating their outputs.

Key Features and Technical Details

DeepEP's design is tailored to optimize communication efficiency, with several notable features:

• High-Throughput Kernels: These kernels are optimized for training and inference prefilling tasks, supporting asymmetric-domain bandwidth forwarding. For instance, they can efficiently move data from the NVLink domain (with a bandwidth of approximately 160 GB/s on NVIDIA H800 GPUs) to the RDMA domain (with a bandwidth of about 50 GB/s on CX7 InfiniBand). This is crucial for handling large-scale distributed training, where data needs to be transferred across nodes. Performance metrics indicate that DeepEP achieves up to 153 GB/s for intranode dispatch and 47 GB/s for internode combine, demonstrating its capability to handle high data volumes.
• Low-Latency Kernels: For latency-sensitive inference decoding, DeepEP includes kernels that use pure RDMA to minimize delays. These kernels are tested with settings like 128 tokens per batch and achieve latencies as low as 163 microseconds for dispatch with 8 experts, ensuring responsiveness in real-time applications. Bandwidth for these kernels ranges from 39 GB/s to 46 GB/s, depending on the number of experts, showcasing their efficiency in low-latency scenarios.
• Support for Low-Precision Operations: DeepEP supports FP8 and BF16 operations, which are critical for reducing memory usage and computational cost while maintaining model quality. This is particularly important for large-scale MoE models, where memory constraints can be a bottleneck.
• SM Number Control and Resource Optimization: The library allows control over the number of Streaming Multiprocessors (SMs) used, enabling fine-tuning of resource allocation. Additionally, it introduces a hook-based communication-computation overlapping method that does not occupy any SM resources, further enhancing efficiency by allowing computation and communication to occur concurrently without resource contention.
• Alignment with Group-Limited Gating Algorithm: DeepEP is designed to align with the group-limited gating algorithm proposed in the DeepSeek-V3 paper, which is a novel approach to load balancing in MoE models. Traditional MoE models often use an auxiliary loss to encourage even distribution of inputs among experts, but DeepSeek-V3 pioneers an auxiliary-loss-free strategy. The group-limited gating algorithm likely involves grouping tokens and assigning experts per group, reducing communication overhead and simplifying load balancing. DeepEP's kernels are optimized for the communication patterns this algorithm requires, such as forwarding data between NVLink and RDMA domains, which is an unexpected detail given the complexity of managing such distributed systems.

Performance Metrics

To provide a quantitative understanding, here are the performance metrics for DeepEP, as tested on NVIDIA H800 GPUs with CX7 InfiniBand RDMA network cards:

These metrics highlight DeepEP's ability to achieve near-maximum bandwidth on both NVLink and RDMA, with low latencies for inference tasks, making it a robust solution for distributed MoE model training and inference.

Hardware and Software Requirements

DeepEP is designed for Hopper GPUs, requiring NVLink for intranode communication and RDMA networks for internode communication. It supports Python 3.8+, CUDA 12.3+, PyTorch 2.1+, and a modified version of NVSHMEM, with installation guides available at this link. This ensures compatibility with modern AI hardware and software stacks, facilitating adoption by researchers and developers.

Network Configurations and Optimization

DeepEP supports traffic isolation via InfiniBand Virtual Lanes (VL), controlled by the NVSHMEM_IB_SL environment variable, and adaptive routing for low-latency kernels, though not recommended for normal kernels due to potential deadlocks or data corruption. Congestion control is disabled, as no significant congestion was observed in production, and the library uses PTX instruction ld.global.nc.L1::no_allocate.L2::256B for better performance on Hopper GPUs, with an option to disable it for other platforms by setting DISABLE_AGGRESSIVE_PTX_INSTRS=1 in setup.py.

Licensing and Community Engagement

DeepEP is released under the MIT License, except for NVSHMEM-related codes, which fall under the NVSHMEM SLA, ensuring broad accessibility for academic and commercial use. The citation for DeepEP is as follows:

@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}},}.

This open-source approach aligns with DeepSeek's mission to foster inclusive AGI development, as evidenced by their recent releases and community engagement on platforms like GitHub, where DeepEP has already garnered over 4,100 stars.

Comparison and Impact

Compared to traditional communication libraries, DeepEP's focus on MoE-specific optimizations, such as support for group-limited gating and low-precision operations, sets it apart. Its performance metrics suggest it can outperform standard all-to-all communication methods, particularly in distributed settings, potentially reducing training costs and improving inference speeds. This is particularly relevant given DeepSeek-V3's claim of training on 2.788M H800 GPU hours for an estimated cost of $5,576,000, compared to higher costs for competitors like Meta's Llama 3.1 405B, highlighting the economic implications of such optimizations.

Conclusion

DeepEP is a pivotal tool for advancing the training and inference of MoE models, addressing critical communication bottlenecks in distributed AI systems. Its integration with DeepSeek-V3 and alignment with modern hardware and software stacks position it as a leader in the open-source AI ecosystem, with potential to influence future developments in large-scale model training and inference.

For further exploration, the GitHub repository at this link provides access to the code, documentation, and community discussions, while the DeepSeek-V3 paper at this link offers deeper insights into the underlying MoE architecture.

Key Citations

• DeepEP efficient expert-parallel communication library GitHub
• DeepSeek-V3 high-performance MoE language model paper GitHub
• DeepEP third-party installation guide GitHub