Introduction to DeepSeek Architecture
Before you understand why DeepSeek is different, you need to know what every LLM shares. MLA, MoE, MTP — the three innovations that matter.
Each series is a connected sequence of articles that builds from foundations to mastery. Pick one and start reading.
From Transformer internals to DeepSeek-V4. 10 planned phases covering every architectural innovation.
Four papers. Four years. From IO-aware tiling to Blackwell asymmetric scaling.
From Transformer internals to DeepSeek-V4. A complete technical deep-dive into every architectural innovation — MLA, MoE, MTP, FP8 training, GRPO, and more. Grounded in original papers and code.
Build the mental model of how a Large Language Model works — from raw text to generated tokens. No assumed ML background.
Before you understand why DeepSeek is different, you need to know what every LLM shares. MLA, MoE, MTP — the three innovations that matter.
LLMs don't read words. They read numbers. The full journey from your sentence to token IDs to embeddings to predictions and back.
The attention mechanism is the one idea that changed AI forever. Query, Key, Value — explained without assuming you know linear algebra.
Stop using attention as a black box. Every matrix multiplication explained — Q, K, V projections, scaled dot-product, and a worked numeric example.
Why can't an LLM see into the future? The causal mask is why — and it's elegantly simple. Plus: the full autoregressive loop explained.
One attention head learns one pattern. GPT-4 has 96 of them. Here's why multiple heads matter and what each one actually learns.
Theory is nice. Running code is better. Full PyTorch MHA from scratch — no nn.MultiheadAttention, just raw matrix ops.
Explain the #1 memory bottleneck in LLM inference (the KV cache) and trace the evolution of attention variants: MQA → GQA → MLA.
4 articles · coming soon
The centrepiece technical innovation of DeepSeek-V2/V3 — Multi-Head Latent Attention. 93% KV cache reduction without quality loss.
5 articles · coming soon
How Transformers understand word order — from integer position to RoPE. The modern standard used by DeepSeek, LLaMA, and Mistral.
5 articles · coming soon
DeepSeek's second pillar — fine-grained expert segmentation, shared experts, and auxiliary-loss-free load balancing.
7 articles · coming soon
Predicting multiple future tokens simultaneously — improving training efficiency and enabling speculative decoding.
6 articles · coming soon
DeepSeek-V3 was the first 671B model to train with FP8. Fine-grained tile-based quantization explained.
6 articles · coming soon
Training 671B parameters on 2048 GPUs — parallelism strategies, communication topology, and the $5.58M budget.
8 articles · coming soon
The most impactful release — RL for reasoning, GRPO algorithm, emergent chain-of-thought, and test-time compute scaling.
6 articles · coming soon
V4, million-token context, agentic architectures, and the future of open-source frontier models.
6 articles · coming soon