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DeepSeek-V3 Technical Report

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Type
Open Source
Company
DeepSeek-AI
Visit Website GitHub (40 stars)

About DeepSeek-V3 Technical Report

DeepSeek-V3 is a powerful Mixture-of-Experts (MoE) language model developed by DeepSeek-AI. It features 671 billion total parameters with 37 billion activated per token, enabling efficient inference and cost-effective training. The model adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, validated in earlier versions, and introduces an auxiliary-loss-free strategy for load balancing along with a multi-token prediction training objective for enhanced performance. Pre-trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 undergoes Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations show it outperforms other open-source models and achieves performance comparable to leading closed-source models.

Key Features

Mixture-of-Experts (MoE) architecture with 671B total parameters and 37B activated per token
Multi-head Latent Attention (MLA) for efficient inference
DeepSeekMoE architecture for cost-effective training
Auxiliary-loss-free load balancing strategy
Multi-token prediction training objective
Pre-trained on 14.8 trillion diverse tokens
Supervised Fine-Tuning and Reinforcement Learning stages

Pros & Cons

Pros
  • Outperforms other open-source models across various benchmarks
  • Competitive with leading closed-source models
  • Efficient inference with 37B activated parameters despite 671B total
  • Innovative training strategies (auxiliary-loss-free load balancing, multi-token prediction)
  • Open-source availability enables community use and further research
Cons
  • Very large model size (671B parameters) requires substantial computational resources
  • Not suitable for local deployment on consumer hardware
  • Training on 14.8T tokens demands significant energy and cost

Best For

General language understanding and generationCode generation and reasoningQuestion answering and dialogueText summarization and translationMathematical and logical reasoning

FAQ

What architecture does DeepSeek-V3 use?
DeepSeek-V3 uses a Mixture-of-Experts (MoE) architecture with Multi-head Latent Attention (MLA) and DeepSeekMoE, building on the design validated in DeepSeek-V2.
How many parameters does DeepSeek-V3 have?
DeepSeek-V3 has 671 billion total parameters, with 37 billion activated for each token.
What training data was used for DeepSeek-V3?
DeepSeek-V3 was pre-trained on 14.8 trillion diverse and high-quality tokens.
How does DeepSeek-V3 compare to other models?
Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models.
Is DeepSeek-V3 open source?
Yes, DeepSeek-V3 is an open-source model, as indicated by the tool type and the availability of the technical report on arXiv.