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Local LLMs: Memory, Quantization and Training

Begin with memory arithmetic, experiment with numerical error, then distinguish adapters from the frozen base model.

By Sandeep Belgavi · Learning path · Updated 28 October 2026

Begin with memory arithmetic, experiment with numerical error, then distinguish adapters from the frozen base model.

Recommended sequence

  1. How quantization changes the memory budget

    Beginner · no prerequisites. Evidence: Explanation + calculation.

  2. Why loading a model does not prove a request fits

    Beginner · weight-memory basics. Evidence: Explanation + cache-budget lab.

  3. Compare four-bit and eight-bit numerical error

    Intermediate · basic rounding. Evidence: Explanation + synthetic vector lab.

  4. Inspect what LoRA and QLoRA train

    Intermediate · matrix dimensions. Evidence: Explanation + parameter-count lab.

  5. Understand the serving architecture

    Intermediate · inference-memory basics. Evidence: Technical explanation.

The interactive calculations run in the article pages. They use synthetic inputs and do not contact a model. Measured GPU results are added only after successful recorded runs.

How to interpret evidence

An explanation develops a concept using references and reasoning. A simulation shows the output of a simplified model with explicit assumptions. A measured experiment reports an actual run, its configuration, sample size and limitations. A simulation does not establish production performance. Follow each guide’s prerequisites and check the stated expected outcome before changing an input.

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