DeepSeek-V3
| Property | Value |
|---|---|
| Total Parameters | 671B |
| Active Parameters | 37B |
| Context Length | 128K tokens |
| License | MIT (code) + Model License |
| Paper | arXiv:2412.19437 |
What is DeepSeek-V3?
DeepSeek-V3 represents a significant advancement in large language model development, utilizing a Mixture-of-Experts (MoE) architecture with 671B total parameters, of which 37B are activated for each token. The model was pre-trained on 14.8 trillion diverse tokens and incorporates innovative features like Multi-head Latent Attention (MLA) and an auxiliary-loss-free load balancing strategy.
Implementation Details
The model leverages pioneering FP8 mixed precision training framework and employs a Multi-Token Prediction (MTP) objective. Its training process required only 2.788M H800 GPU hours, demonstrating remarkable efficiency and stability with no irrecoverable loss spikes.
- Efficient architecture with MoE and MLA components
- Novel load balancing strategy without auxiliary loss
- Advanced FP8 training framework
- 128K context window capability
Core Capabilities
- Superior performance in mathematical and coding tasks
- Strong multilingual abilities, particularly in English and Chinese
- Excellent reasoning capabilities through knowledge distillation from DeepSeek-R1
- Commercial-use support with flexible deployment options
Frequently Asked Questions
Q: What makes this model unique?
DeepSeek-V3's distinctive feature is its efficient MoE architecture combined with FP8 training, achieving SOTA performance while maintaining reasonable computational requirements. The model particularly excels in mathematical and coding tasks, often outperforming larger dense models.
Q: What are the recommended use cases?
The model is well-suited for complex reasoning tasks, mathematical problem-solving, code generation, and multilingual applications. It supports both research and commercial applications, with various deployment options including local installation and API access.
