Build DeepSeek from Scratch
A comprehensive hands-on guide covering every major DeepSeek innovation — from KV Cache and Multi-Head Latent Attention to Mixture-of-Experts, Multi-Token Prediction, FP8 Quantization, Sparse Attention, Manifold-Constrained Hyper-Connections, Conditional Memory (Engram), and the million-token DeepSeek-V4 architecture.
In this chapter:
This book guides you through building the key components of the DeepSeek language model family from scratch. Based on research papers from DeepSeek-AI spanning V2 through V4, each part combines theory with practical PyTorch implementations.
DeepSeek represents a turning point in open-source AI. By the end of this book, you will not only understand what makes each DeepSeek generation unique but also how to implement those innovations yourself.
What you’ll build
- Part 1: Introduction — Why DeepSeek matters and a high-level roadmap
- Part 2: KV Cache — Solving the inference bottleneck, from MHA to MQA and GQA
- Part 3: Multi-Head Latent Attention — DeepSeek’s breakthrough 64× memory reduction
- Part 4: Mixture-of-Experts — Scaling intelligence efficiently with sparse expert routing
- Part 5: MTP & FP8 Quantization — Advanced training objectives and numerical precision
- Part 6: DeepSeek Sparse Attention (DSA) — O(L·k) complexity via lightning indexing
- Part 7: mHC — Manifold-Constrained Hyper-Connections for stable deep networks
- Part 8: Engram — Conditional memory as a second axis of sparsity
- Part 9: DeepSeek-V4 — Hybrid CSA/HCA attention for million-token contexts
Paper Sources
- DeepSeek-V2 / V3 / R1 papers (2024-2025)
- DeepSeek-V3.2: Pushing the Frontier of Open LLMs (Dec 2025)
- mHC: Manifold-Constrained Hyper-Connections (Dec 2025)
- Engram: Conditional Memory via Scalable Lookup (Jan 2026)
- DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence (Apr 2026)