DeepSeek #OpenSourceWeek Day 2: Release of DeepEP

DeepSeek is here with its Day 2 of #OpenSourceWeek and today they introduced DeepEP – An open Source EP communication library for MOE model training and inference. Till now, I have been completely impressed by DeepSeek and their answer to the billion-dollar models of OpenAI, Meta and more. Now, they are open-sourcing the building blocks in exploring AGI. With the 5 repos (2 already released) they are showcasing the commitment to transparency, community collaboration and advancement in AI.

On Day 1 team at DeepSeek released FlashMLA and you can read about it here – DeepSeek #OpenSourceWeek Day 1: Release of FlashMLA.

Today, we are going to talk about the DeepEP in detail.

Key Highlights of the Release

• Efficient and optimized all-to-all communication
• Both Intranode and internode support with NVLink and RDMA
• High-throughput kernels for training and inference prefilling
• Low-latency kernels for inference decoding
• Native FP8 dispatch support
• Flexible GPU resource control for computation-communication overlapping

DeepEP: Optimized Communication Library for MoE and Expert Parallelism

DeepEP is a high-performance communication library designed specifically for Mixture-of-Experts (MoE) and expert parallelism (EP). It features highly efficient all-to-all GPU kernels—commonly referred to as MoE dispatch and combine—delivering exceptional throughput and minimal latency. Additionally, DeepEP supports low-precision computations, including FP8, ensuring flexibility in deep learning workloads.

To complement the group-limited gating algorithm introduced in the DeepSeek-V3 paper, DeepEP provides specialized kernels tailored for asymmetric-domain bandwidth forwarding. These kernels optimize data transfers between different hardware domains, such as NVLink and RDMA, maximizing throughput for both training and inference prefilling tasks. Moreover, the library includes built-in controls for managing Streaming Multiprocessors (SM) usage.

For inference scenarios that demand ultra-low latency, particularly during decoding, DeepEP integrates a dedicated set of RDMA-only kernels to significantly reduce communication delays. Additionally, it employs an innovative hook-based approach to overlap communication with computation—without consuming any SM resources—ensuring optimal efficiency.

Why DeepSeek is OpenSourcing it?

DeepSeek’s decision to open-source its technology is all about making cutting-edge AI accessible to everyone. By sharing its innovations, it empowers developers, researchers, and businesses across industries—whether in healthcare, climate science, or defence—to push boundaries and build even more advanced solutions. Open access fosters collaboration speeds up breakthroughs, and ensures that AI development isn’t limited to a select few.

DeepEP is the “first open-source EP communication library for MoE model training and inference.”

And the best part? DeepSeek’s tools are available on GitHub, making it easy for anyone to explore, contribute, and refine the technology further.

Now, let’s understand what is Mixture of Experts (MoE)

What is a Mixture of Experts (MoE)?

The size of a model plays a crucial role in determining its quality. With a fixed computational budget, it is generally more effective to train a larger model for fewer steps rather than a smaller model for more steps. This is where Mixture of Experts (MoE) comes into play – it allows models to scale significantly while optimizing computational efficiency.

MoE is a neural network architecture designed to optimize model training and inference by selectively activating only a subset of parameters during computation. This enables the use of much larger models without a proportional increase in computational cost.

MoE Primarily Consists of Two Key Components

1. Sparse MoE Layers – These replace traditional dense feed-forward network (FFN) layers. Instead of a single FFN, MoE layers consist of multiple experts (e.g., 8 separate networks). Each expert functions as a standalone neural network, typically an FFN, but in some cases, these experts can be more complex structures or even hierarchical MoEs.
2. Router or Gate Network – This mechanism determines which tokens are assigned to which experts. For instance, in a given sequence, one token might be directed to Expert 2, while another is processed by Expert 1. A key design choice in MoE is how tokens are distributed among experts. The routing mechanism is governed by learnable parameters that are trained alongside the rest of the model.

How Does MoE Work in Transformer Models?

In a standard transformer model, every token is processed through dense FFN layers. However, in MoE models, these dense FFN layers are replaced with MoE layers, consisting of multiple experts and a gating mechanism. During inference and training, only a subset of these experts is activated per token, reducing overall computation while maintaining model capacity.

Benefits of MoE Models

• Efficient Pretraining – MoE enables pretraining large models with significantly lower compute requirements compared to dense models, allowing researchers to train models faster without excessive hardware costs.
• Faster Inference – Since only a portion of the model’s parameters is used at any given time, the inference is considerably more efficient compared to a dense model of equivalent total size.
• Scalability – MoE allows researchers to increase the model size and dataset size while staying within the same compute budget as a dense model.

The Mixture of Experts (MoE) is a powerful approach for scaling transformer models efficiently, making it possible to train massive models with reduced computational costs. By replacing traditional dense FFN layers with sparse MoE layers and utilizing a routing mechanism, these models achieve high scalability and improved inference speeds. However, the trade-offs include increased memory demands, training complexities, and the challenge of designing an effective routing strategy. As research continues, MoE-based architectures are likely to play a significant role in the next generation of AI models.

How OpenSourcing DeepEP is a Game Changer and What it Offers?

1. Efficient and optimized all-to-all communication

To efficiently train and deploy MoE models, seamless communication between nodes is essential—both within a single machine (Intranode) and across multiple machines (internode). DeepEP addresses this challenge with highly optimized all-to-all communication, ensuring fast and efficient data transfer, minimizing bottlenecks, and maximizing performance.

2. Intranode and Internode support with NVLink and RDMA

DeepEP goes beyond basic communication, enabling seamless Intranode and internode connectivity through advanced technologies like NVLink and RDMA (Remote Direct Memory Access). NVLink, NVIDIA’s high-speed interconnect, accelerates data exchange within nodes, while RDMA minimizes latency in cross-node transfers, ensuring optimal performance for large-scale AI systems. These innovations collectively redefine efficiency, making DeepEP a powerhouse for next-generation AI workloads.

3. High-throughput kernels for training and inference prefilling

DeepEP is designed to handle large-scale data efficiently. Its high-speed kernels enable rapid training by optimizing how data moves through the system. During inference prefilling, these kernels process large batches swiftly, ensuring smooth and efficient performance without bottlenecks.

4. Low-latency kernels for inference decoding

When it comes to real-time predictions, speed is everything. DeepEP’s low-latency kernels minimize delays during inference decoding, delivering instant responses with minimal lag. This makes it ideal for applications that demand quick decision-making and seamless user experiences.

5. Native FP8 dispatch support

DeepEP stands out with its built-in FP8 (Floating Point 8) support, a cutting-edge format that boosts speed and reduces memory use—perfect for scaling AI models. By integrating FP8, DeepSeek ensures the library stays ahead of evolving AI hardware and algorithms. This means faster training, lower energy costs, and a more efficient path toward sustainable AI development.

6. Flexible GPU resource control for computation-communication overlapping

DeepEP optimizes GPU usage by enabling simultaneous computation and data transfer, minimizing downtime and maximizing performance. Ideal for large-scale AI projects, it helps researchers and businesses save time and costs while scaling efficiently.

Try DeepEP YourSelf

Visit the GitHub Repository – Find DeepEP’s source code, docs, and examples on GitHub to get started quickly.

Explore the Documentation – Learn how to utilize DeepEP’s key features like NVLink, RDMA, and FP8 with clear, step-by-step guidance.

Finally, you can leverage any tool to test and integrate DeepEP.

Conclusion

DeepSeek released DeepEP on Day 2 of Open Source Week. It’s a game-changer for Mixture of Experts (MoE) model training and inference. DeepSeek offers a high-performance, open-source EP communication library. It boosts efficiency, cuts latency, and improves resource management for large-scale AI workloads. DeepEP supports NVLink, RDMA, FP8, and seamless computation-communication overlap. This empowers developers and researchers to advance AI innovation. DeepSeek’s open-source commitment speeds up AGI progress. It makes cutting-edge AI tools more accessible globally.

Stay tuned to Analytics Vidhya Blog for our detailed analysis on DeepSeek’s Day 3 release!

Pankaj Singh

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.