DeepSeek DeepEP

DeepEP is a specialized communication library designed specifically for Mixture-of-Experts (MoE) models and expert parallelism (EP)

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Features

DeepEP - Professional Distributed Communication Framework

DeepEP is a next-generation distributed communication framework specifically optimized for Mixture-of-Experts (MoE) and Expert Parallelism (EP) scenarios. Our framework provides high-throughput, low-latency GPU all-to-all communication kernels, perfectly supporting MoE dispatch and combine operations.

DeepEP's Innovative Technical Advantages

DeepEP supports low-precision operations including FP8 and provides optimizations for the group-limited gating algorithm proposed in DeepSeek-V3. Our framework specially supports efficient data transmission between heterogeneous domains such as NVLink to RDMA, ensuring excellent performance for training and inference prefilling tasks.

DeepEP's High-Performance Architecture

Based on pure RDMA technology, DeepEP provides a set of low-latency kernels specifically optimized for inference decoding performance. The unique hook-based communication-computation overlapping method achieves excellent parallel efficiency without occupying SM resources.

DeepEP's Flexible Scalability

DeepEP framework supports flexible SM number control and provides rich configuration options. Our system can dynamically adjust resource allocation based on actual needs, maximizing hardware performance.

DeepEP's Enterprise-Grade Reliability

As an enterprise-level distributed framework, DeepEP provides stable and reliable performance guarantees. Our system has undergone rigorous testing to ensure stable operation in various complex scenarios, meeting enterprise-level application requirements.

DeepEP's Technical Ecosystem Support

DeepEP continuously follows the latest technological developments, providing comprehensive technical support and documentation. Our team is committed to continuously optimizing framework performance, providing users with the best distributed computing solutions.

Prompt Engineering Tips: Unleashing the Power of Mixture of Experts

To fully leverage DeepSeek's Mixture of Experts (MoE) model, explicitly tell it which expert roles you want it to assume when answering your questions. Here are some effective prompting strategies.

Expert Team Strategy

Specify a team of experts from different domains to get responses from multiple professional perspectives.

Example:

Assume you are a team of experts consisting of: 1. Distributed Systems Architect 2. E-commerce Business Architect 3. Senior DBA. Please provide technical recommendations from each perspective.

Clear Task Requirements

Provide specific performance metrics and technical requirements to guide more targeted responses.

Example:

Design a system that can handle 100,000 transactions per second, ensures eventual data consistency, and has a recovery time of less than 30 seconds.

Domain Knowledge Activation

Mention specific domain terminology and concepts to activate the model's expertise in relevant areas.

Example:

Which distributed consistency protocols and caching strategies would you consider to ensure data consistency while maximizing system throughput?

Effectiveness Comparison

❌ Ineffective Prompt

How to design a high-concurrency, highly available system?

✅ Effective Prompt

Assume you are a team of experts consisting of: 1. Distributed Systems Architect 2. E-commerce Business Architect 3. Senior DBA. Please provide 5 technical recommendations from each perspective and design a combined solution that can handle 100,000 transactions per second, ensures eventual data consistency, and has a recovery time of less than 30 seconds.

FAQs

Here are some of the most frequently asked questions.

DeepEP is a specialized communication library designed for Mixture-of-Experts (MoE) models and expert parallelism. DeepEP provides high-performance GPU kernels for all-to-all communication, optimizing MoE dispatch and combine operations. The framework supports low-precision operations including FP8 and implements advanced algorithms for efficient data transmission between heterogeneous domains.

DeepEP offers several innovative features: high-throughput GPU communication kernels, support for group-limited gating algorithms, efficient data transmission between NVLink and RDMA domains, and low-latency inference decoding. DeepEP's unique hook-based communication-computation overlapping method achieves excellent parallel efficiency without occupying SM resources.

DeepEP significantly improves distributed system performance through its optimized communication patterns and efficient resource utilization. The framework's architecture enables high-throughput data transfer while maintaining low latency. DeepEP's specialized kernels and innovative algorithms ensure optimal performance for both training and inference tasks in distributed environments.

DeepEP stands out with its specialized focus on MoE and expert parallelism scenarios. Unlike general-purpose communication libraries, DeepEP provides optimized kernels specifically designed for AI model training and inference. The framework's support for advanced features like FP8 operations and group-limited gating makes it particularly effective for modern AI applications.

DeepEP is designed for seamless integration with existing distributed systems. The framework provides comprehensive documentation and flexible configuration options. DeepEP's architecture supports various deployment scenarios, and its modular design allows for easy customization based on specific requirements. The framework's enterprise-grade reliability ensures stable operation in production environments.

DeepEP offers extensive technical support and documentation. Our team continuously updates the framework with the latest technological developments and provides comprehensive guidance for implementation. DeepEP's technical ecosystem includes detailed documentation, example implementations, and regular updates to ensure optimal performance and compatibility.