Understanding Modern LLM Architectures
A guided journey from transformer fundamentals to the cutting edge of LLM engineering. You will build an intuition for how modern language models are designed, scaled, optimized, and deployed, by covering attention mechanisms, Mixture of Experts, architectural innovations like DeepSeek's MLA, reasoning capabilities, inference optimization, and the open-source ecosystem reshaping AI.
Understanding Transformer Architectures from Scratch
Start here. Learn the foundational building blocks that power every modern LLM.
Mixture of Experts Demystified: Why Every Frontier Model Uses MoE Now
Now that you understand transformers, see how Mixture of Experts lets models scale to hundreds of billions of parameters without proportional compute cost.
Inside DeepSeek: The Architecture Innovations That Shook the AI Industry
Apply what you learned about MoE and attention to a real architecture. DeepSeek introduces Multi-head Latent Attention (MLA) and Multi-Token Prediction, innovations that push efficiency further.
Reasoning Models: How LLMs Learned to Think Before They Speak
Shift from architecture to capability. Understand how chain-of-thought prompting and test-time compute allow LLMs to reason through complex problems step by step.
LLM Inference Optimization: The Engineering Behind Fast, Cheap AI
With the architecture and reasoning foundations covered, learn techniques such as quantization, speculative decoding, KV-cache optimization, that make inference fast and affordable.
The Open-Source LLM Power Shift: How Qwen, DeepSeek, and Mistral Changed Everything
Zoom out to the full landscape. See how open-source models from DeepSeek, Qwen, and Mistral are reshaping the industry, and where the field is heading.