Introduction to DeepSeek-V3 Series

This is the fourth article in our DeepSeek-V3 series, where we explain the final major architectural innovation in DeepSeek models: multi-token prediction. Originally published on Towards AI.

Background and Previous Articles

In previous articles, we explained how DeepSeek carefully balances various architectural trade-offs, including:

• Multi-head Latent Attention, which optimizes memory efficiency while maintaining model performance during decoding.
• DeepSeekMoE, which balances knowledge sharing and expert specialization within the Mixture of Experts (MoE) architecture.
• Auxiliary-Loss-Free Load Balancing, which achieves effective load balancing without compromising the main training objective.

What is Multi-Token Prediction?

In this article, we will explore how DeepSeek strikes yet another balance — between efficiency and quality in text generation. We will introduce the fundamentals of the decoding process in LLMs, focusing on how next-token prediction works and its limitations. We also review prior works on multi-token prediction (MTP), discussing the design choices, as well as the advantages and limitations of these approaches.

DeepSeek’s Multi-Token Prediction

DeepSeek’s Multi-Token Prediction strategy will be explained in detail, including how it works and the design choices behind it. We will discuss how it differs from prior works and introduce how DeepSeek’s MTP strategy can be combined with speculative decoding to accelerate inference.

Evaluation and Impact

We will discuss the impact of MTP on both training performance and inference efficiency, providing insights into the benefits and potential drawbacks of this approach.

Table of Contents

The article is organized into the following sections:

• Background: Introduction to the decoding process in LLMs and prior works on MTP.
• DeepSeek’s Multi-Token Prediction: Explanation of how it works and its design choices.
• Evaluation: Discussion of the impact of MTP on training performance and inference efficiency.
• Summary and Reference.

Other Articles in the DeepSeek Series

Other articles in the series include:

• Part 1: Multi-head Latent Attention.

Conclusion

In conclusion, DeepSeek’s multi-token prediction strategy offers a promising approach to balancing efficiency and quality in text generation. By understanding how it works and its design choices, we can better appreciate the potential benefits and limitations of this approach.

Frequently Asked Questions

Here are some frequently asked questions about DeepSeek and multi-token prediction:

• Q: What is DeepSeek?
  • A: DeepSeek is a series of articles exploring the architectural innovations in DeepSeek models.
• Q: What is multi-token prediction?
  • A: Multi-token prediction is a strategy used in LLMs to predict multiple tokens at once, rather than one token at a time.