DeepSeek-V3-NextN
| Property | Value |
|---|---|
| Total Parameters | 671B |
| Activated Parameters | 37B |
| Context Length | 128K tokens |
| Architecture | Mixture-of-Experts (MoE) |
| License | MIT (Code), Custom Model License |
| Paper | arXiv:2412.19437 |
What is DeepSeek-V3-NextN?
DeepSeek-V3-NextN represents a significant advancement in large language model architecture, featuring a massive 671B parameter count with only 37B parameters activated per token. This model introduces innovative approaches including Multi-head Latent Attention (MLA) and an auxiliary-loss-free load balancing strategy, trained on 14.8 trillion diverse tokens using FP8 precision.
Implementation Details
The model leverages state-of-the-art architectural innovations including:
- FP8 mixed precision training framework - first validation at extreme scale
- Multi-Token Prediction (MTP) objective for enhanced performance
- DeepSeekMoE architecture with efficient load balancing
- Optimized cross-node training with near-perfect computation-communication overlap
Core Capabilities
- Superior performance on math and code tasks compared to other open-source models
- Strong multilingual capabilities with high performance on Chinese benchmarks
- 128K context window with maintained performance across lengths
- Efficient inference options through multiple frameworks (SGLang, LMDeploy, TRT-LLM)
- Commercial use support with comprehensive deployment options
Frequently Asked Questions
Q: What makes this model unique?
DeepSeek-V3's uniqueness lies in its efficient MoE architecture that activates only 37B parameters while maintaining a total of 671B parameters, combined with innovative FP8 training and auxiliary-loss-free load balancing. It achieves this while requiring only 2.788M H800 GPU hours for training.
Q: What are the recommended use cases?
The model excels in complex reasoning tasks, mathematical problem-solving, code generation, and multilingual applications. It's particularly well-suited for enterprise applications requiring high accuracy in specialized domains while maintaining efficient resource usage.
