DeepSeek-V2, an advanced open-source Mixture-of-Experts (MoE) language model has been designed for economical training and efficient inference. DeepSeek-V2 boasts a total of 236 billion parameters, of which only 21 billion are activated per token, achieving high efficiency while maintaining exceptional performance across various benchmarks. It supports an extended context length of up to 128K tokens, addressing the challenge of long-context processing in language models.

Key Innovations and Architectural Features
Multi-Head Latent Attention (MLA):
MLA replaces the traditional Multi-Head Attention (MHA) to address the inefficiencies caused by the Key-Value (KV) cache during inference.
It uses low-rank key-value joint compression, significantly reducing the KV cache size while outperforming MHA in performance.
DeepSeekMoE Architecture:
This architecture specializes in sparse computation by segmenting experts into finer granularity and isolating shared experts. It enables efficient training and better parameter utilization compared to traditional MoE architectures.
Training Efficiency:
DeepSeek-V2 reduces training costs by 42.5% compared to its predecessor, DeepSeek 67B.
It achieves a 5.76x boost in inference throughput and a 93.3% reduction in KV cache requirements, making it highly scalable for deployment.
Pre-Training and Fine-Tuning:
Pre-trained on 8.1 trillion tokens, the model incorporates a diverse and high-quality corpus.
Subsequent supervised fine-tuning and reinforcement learning are employed to align the model with human preferences and task-specific objectives.
Performance and Evaluation
DeepSeek-V2 delivers top-tier performance on multiple benchmarks, including:
English Benchmarks: Outperforms or matches other leading open-source models like LLaMA and Qwen in tasks such as reading comprehension, reasoning, and commonsense knowledge.
Code and Math Benchmarks: Demonstrates comparable or superior performance with fewer activated parameters.
Chinese Benchmarks: Achieves state-of-the-art results, reflecting its robust bilingual capabilities.
Training Infrastructure
The model is trained using an internally developed HAI-LLM framework with optimizations such as:
Zero-bubble pipeline parallelism and expert parallelism to reduce communication overhead.
Custom CUDA kernels for improved computational efficiency.
Advantages of DeepSeek-V2
Cost Efficiency: Reduces GPU resource consumption significantly during training and inference.
Scalability: Handles long-context tasks seamlessly with its 128K token context window.
High Performance: Excels in multilingual and multi-domain tasks with fewer activated parameters.
Open Source: Offers transparency and accessibility to researchers and developers, with its model and methodologies publicly available.
Conclusion
DeepSeek-V2 sets a new benchmark for Mixture-of-Experts language models by combining economical training, efficient inference, and exceptional task performance. Its innovative architecture, particularly the MLA and DeepSeekMoE, addresses the key challenges in large-scale language model development. This research represents a significant step toward making advanced AI models more accessible and practical for real-world applications.