Table of Contents

DeepSeek-V3 from Scratch: Building the Architecture in PyTorch

The Transformer Block: Combining MLA and MoE

The Complete Model: From Tokens to Logits

Weight Tying: Sharing Token Embeddings and Output Weights

Multi-Token Prediction Integration

Implementing the DeepSeek-V3 Architecture in PyTorch

Architectural Design Patterns

Parameter Count Analysis

Memory and Computation Footprint

Summary Citation Information

Across the first 4 lessons, we have carefully constructed the building blocks of DeepSeek-V3: starting with its configuration and Rotary Position Embeddings (RoPE) , advancing through Multihead Latent Attention (MLA) , scaling capacity with the Mixture of Experts (MoE) , and using Multi-Token Prediction (MTP) to improve training efficiency and enable faster inference through speculative decoding.…