How to Choose an AI Tool for Your Actual Workflow: A Practical Selection Guide
The right AI tool is the one that fits a specific task you already do, survives a fair test on your own examples, and earns a place…
FROM THE AUTHOR
John C. Mullen
Explore the published guides and contributions from John C. Mullen.
46 published articlesRAM vs VRAM for Local AI: What Each One Does
When you run a local LLM, VRAM is where inference happens on the GPU, while system RAM provides capacity for loading models, CPU inference, and offloading. Both…
Connecting to Ollama from Another Device on Your Network
Ollama listens on 127.0.0.1:11434 by default, so nothing outside the host machine can reach it. Making it available to a phone, laptop, or second PC means changing…
How to Compare Local Language Models with Reproducible Tests
A reproducible local model comparison pins the model, runtime, hardware, prompt template, context settings, and sampling parameters before measuring quality, latency, memory, and failure rates. This guide…
AI Hardware Explained: RAM, VRAM, Quantization, and Model Size
Running a local language model is mostly a memory-planning problem. This guide explains how parameter count, quantization, VRAM, RAM, context length, and runtime overhead determine whether a…
How Large Language Models Work: Tokens, Context, Training, and Inference
Large language models are next-token predictors, and almost every practical constraint they have follows from four ideas: how text becomes tokens, how the context window limits what…
How to Record a Reproducible Local Model Benchmark
A reproducible local model benchmark is a record, not a number. It needs the exact model bytes and hashes, the runtime version, the hardware and power state,…
How to Compare Two Prompts Using the Same Test Inputs
A fair prompt comparison changes one thing: the prompt. Hold the test inputs, scoring rubric, repetitions, tool access, and model settings steady, record the full configuration, and…
Ollama Setup and Troubleshooting: A Practical Beginner’s Guide
Ollama can run local models, connect to cloud models, and serve desktop apps, coding agents, and APIs. This guide explains the setup paths, first working commands, log…
RAG vs Fine-Tuning: How to Choose an Approach for Your Project
Start with retrieval augmented generation (RAG) when your project must answer questions from documents that change and cite sources. Choose fine-tuning when the model needs to match…