You’ve seen the numbers. 7B. 70B. 405B. Everyone talks about parameter counts. But what are they? Why does size matter? And what actually happens when you hit “Generate”? This post covers the mechanics: what parameters are, where they live in the model architecture, how scaling affects them, and what that means if you’re running or choosing […]
Read More →Running LLMs locally has become a normal part of how developers work. Two tools dominate this space: llama.cpp and Ollama. They look like competitors, but the relationship is more direct — Ollama is built on top of llama.cpp. This post covers the technical differences, where each performs better, and when to use one versus the other. Table of […]
Read More →A plain-English reference guide covering the jargon that shows up every time a new language model drops, from parameter counts to quantization methods. Contents 01 · Architecture & Model Design — Transformer · Dense Model · Mixture of Experts · Active Parameters · Feed-Forward Network · Layers · Hidden Dimension · Attention Heads 02 · Attention Mechanisms — Multi-Head Attention · Multi-Query Attention · Grouped-Query Attention · KV Cache · Sliding Window Attention · RoPE · RoPE Theta 03 · Sizing, Scale & Counting — Parameters · Embedding Parameters · Non-Embedding […]
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