  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.
