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---
title: "Quantization and optimization: running open models on smaller hardware"
description: "How quantization and other optimization techniques make it possible to run high-quality open models on far more modest hardware than expected."
date: 2026-07-01
tags: [Optimization, AI architecture]
url: "https://ai.malagoli.me/en/blog/quantizzazione-ottimizzazione-modelli-open"
locale: en_US
---

# Quantization and optimization: running open models on smaller hardware

The biggest model isn't always the best choice. Optimization techniques often deliver the same perceived quality at a fraction of the hardware cost.

A common mistake when planning an AI project is starting from the largest model available, only to discover that it requires infrastructure disproportionate to the value of the use case. The same perceived quality is often achievable with a smaller, properly optimized model — at a fraction of the cost.

## What quantization actually is

A model stores its parameters as numbers with a certain precision (typically 16 bits). Quantization reduces this precision — to 8 bits, or even 4 — proportionally reducing the memory required and often increasing response speed, at a quality cost that for many business tasks is minimal or imperceptible.

## The techniques we use most often

- Formats like GGUF, designed to run quantized models even on CPUs or modest hardware, useful for prototypes and modest volumes.

- GPU quantization techniques (such as GPTQ or AWQ), which maintain good quality while significantly reducing video memory requirements.

- Distillation into smaller models, trained to mimic the behavior of a larger model on a specific task.

- Generation speed-up techniques (such as speculative decoding), useful when perceived latency matters as much as answer quality.

## How much quality is really lost

It depends on the task, and should be honestly measured case by case: on open-ended conversations or highly complex reasoning tasks, the loss can be noticeable; on more narrowly scoped tasks — extraction, classification, RAG over documents — it's often negligible. That's why, before choosing the quantization level for production, we always test on the client's real use case, not on a generic benchmark.

## An illustrative example

In a typical internal-assistant use case over company documentation, moving from an unquantized model to an 8-bit quantized version can cut GPU memory requirements by roughly half, with a quality difference that end-user tests often find indistinguishable. These are indicative figures, not a guarantee: actual sizing should always be verified on the specific project.

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