DeepSeek-V3 Explained Part 4: Multi-Token Prediction 0 like April 22, 2025 Share this post Author(s): Nehdiii Originally published on Towards AI. Vegapunk №04 One Piece Character Generated with ChatGPT This is the fourth article in our DeepSeek-V3 series, where we explain the final major architectural innovation in DeepSeek [1, 2] models: multi-token prediction. In previous articles, we explained how DeepSeek carefully balances various architectural trade-offs: Multi-head Latent Attention optimizes memory efficiency while maintaining model performance during decoding.DeepSeekMoE balances knowledge sharing and expert specialization within the Mixture of Experts (MoE) architecture.Auxiliary-Loss-Free Load Balancing achieves effective load balancing without compromising the main training objective. In this article, we will explore how DeepSeek strikes yet another balance — between efficiency and quality in text generation. Table of contents for this article: Background: 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: Explain how it works and discuss the design choices, with a focus on how it differs from prior works. Additionally, we introduce how DeepSeek’s MTP strategy can be combined with speculative decoding to accelerate inference.Evaluation: Discuss the impact of MTP on both training performance and inference efficiency.Summary.Reference. Other articles in the DeepSeek series: Part 1 : Multi-head… Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post