LLMs, Explained

How large language models are built, trained, and improved, explained for engineers who run them.

LLMS EXPLAINED · 0183%ON THE TOP WORDTHE TEACHER'S NEXT-TOKEN DISTRIBUTION

Distillation: How Small Models Learn from Big Ones

What distillation is, the ways labs distill models, and how they use it on their own models and on others', from primary sources.

LLMS EXPLAINED · 02189 GiB OF N-GRAM MEMORY12.4 KiBREAD PER TOKEN

Engram: Memory You Look Up Instead of Compute

DeepSeek's Engram adds a hashed n-gram memory that a model looks up instead of computing: how it works, why it helps, and how DeepSeek-V4.1 and Qwen serve it.

LLMS EXPLAINED · 0348 LAYERS · 3 LINEAR : 1 SPARSE1/9TRAINING FLOPS

Qwen3.8-Flash-Next, Layer by Layer

Qwen's preview of its next architecture, piece by piece: linear and sparse attention, a gated four-branch residual, 512 experts and an n-gram memory, and what each costs to serve.