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Running local models with attention to memory, speed, and quantization tradeoffs.
16 published articlesRunning 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…