TPEE Toptronic® Prompt Engineering Editor

21. Quantisation and Model Size

Choose between FP16, INT8, INT4, quality, speed, and memory use.

By Jacques Botte, founder of Toptronic®. Last updated 12 September 2026.

The lesson

Quantisation stores model weights with fewer bits. This lowers memory use and can increase speed, but may reduce quality.

A 7B or 13B quantised model can be very useful locally. Larger models may need more VRAM or careful offloading.

The right choice depends on task risk: drafting and summaries tolerate smaller models; deep reasoning and complex coding often need stronger models.

Check yourself

Question 1: What does quantisation reduce?
  1. Number of keyboard keys
  2. Monitor size
  3. All need for review
  4. Bits used to store model weights — correct

Answer: Bits used to store model weights

Lower precision reduces memory use and may improve speed.

Question 2: What is the trade-off of aggressive quantisation?
  1. More VRAM required always
  2. No local support
  3. Possible quality loss — correct
  4. Guaranteed perfect reasoning

Answer: Possible quality loss

Compression can reduce accuracy or reasoning quality.

Question 3: When might a stronger or less-quantised model be needed?
  1. Opening the About dialog
  2. Deep reasoning or complex coding — correct
  3. Changing font color
  4. Saving a file name

Answer: Deep reasoning or complex coding

Higher-risk tasks may need better model quality.

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