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Running local models with attention to memory, speed, and quantization tradeoffs.
22 published articlesConnecting WordPress to local AI means running an OpenAI-compatible inference server on hardware you control and registering it as an AI connector so the WordPress AI Client…
A context window is the amount of input an AI system can process at one time, measured in tokens and including the response it is generating. This…
When WordPress fails to reach a local Ollama or llama.cpp server, the server is usually healthy and the connector is the problem. Reported causes cluster around the…
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…
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…
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…
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…
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…
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,…
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…