We present DeepSeek-V2, a strong Mixture-of-Experts (MoE)  language model characterized by economical training and efficient inference.
It comprises 236B total parameters, of which 21B are activated for each token, and supports a context length of 128K tokens.
DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA)  and DeepSeekMoE.
MLA guarantees efficient inference through significantly compressing the Key-Value (KV)  cache into a latent vector, while DeepSeekMoE enables training strong models at an economical cost through sparse computation.
Compared with DeepSeek 67B, DeepSeek-V2 achieves significantly stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times.
We pretrain DeepSeek-V2 on a high-quality and multi-source corpus consisting of 8.1T tokens, and further perform Supervised Fine-Tuning (SFT)  and Reinforcement Learning (RL)  to fully unlock its potential.
Evaluation results show that, even with only 21B activated parameters, DeepSeek-V2 and its chat versions still achieve top-tier performance among open-source models.
The model checkpoints are available athttps://github.com/deepseek-ai/DeepSeek-V2.