$ tree ./blog/./gen-ai/llm-fundamentals/

Topic how llms work

The foundation under every LLM: tokens, attention, the Transformer architecture, and the path an answer takes.

posts14topics3series2updated2026-10-19

showing 13-14 of 14 postspage 2 of 2

132026-09-05

Using Transformers: From pipeline() to Optimized LLM Deployment

pipeline() is only a thin layer of abstraction. This post opens it up: the tokenizer, the model head, logits, padding and attention masks, then on to device, dtype, the KV cache, continuous batching, quantization, and how to choose between vLLM, SGLang and llama.cpp now that TGI is archived.

#transformers./gen-ai/llm-fundamentals/

29 min 142026-09-02

Transformer from A to Z: Understanding the Foundation of LLMs

A systematic mental model of the Transformer: from tokens and why some languages cost more of them, through Attention, residuals, the FFN and RoPE, to the three architecture families, MoE and the full path of inference.

#transformer./gen-ai/llm-fundamentals/

46 min