# Guide to Running GLM-4.5-Air on a 16GB RAM Machine

How to run large language models on limited hardware?

- **Source:** [Hacker News: Show HN](https://news.ycombinator.com)
- **Published:** 2026-07-23
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://github.com/FedericoTs/quantprobe>
- **On Brifing:** https://brifing.si/s/a7e54db4-1a88-46ce-8f6a-4a58e3b56a6a

## Introduction

Running large language models on limited hardware is a significant challenge for many developers. This article explains how you can run massive models like GLM-4.5-Air on a home computer with 16GB RAM.

## Hardware Optimization

Hardware optimization is crucial for running large models. This article details how to optimize memory and storage layers and how these optimizations impact performance.

## Placement Laws

The article presents four fundamental placement laws for running large language models. These laws determine where every bit and byte should be placed to enhance performance.

## Measurements and Validation

The measurements and validations conducted prove the validity of these laws. Particularly, the alignment of pre-registered predictions with actual measurements enhances the reliability of these laws.

## Sample Hardware

The article includes examples using a 2016 desktop computer (GTX 1060 6 GB · 16 GB DDR4 · SATA SSD). Tests on this hardware demonstrate how the laws can be applied in practice.

## Performance Results

Tests show that the GLM-4.5-Air model runs at 0.19 tok/s when operated from a SATA drive with 16 GB RAM. This result falls within the 0.2–0.3 tok/s band predicted by the laws.

## RAM Overclock

RAM overclocking provides a 52% performance boost in dense operations. This increase aligns with pre-registered predictions, further validating the laws.

## Hybrid Placement

The hybrid placement strategy enables the 30B model to run at 19.3 tok/s. This strategy relies on the effective use of memory and storage layers.

## Innovative Approaches

The article also mentions that innovative approaches like dynamic top-k, semantic paging, and self-speculation were tried and failed. These approaches help define the boundaries of the laws.

## Future Projections

The article discusses how future hardware upgrades could impact performance. For example, adding 16 GB DDR4 could allow the 30B model to run stably at 19–21 tok/s.

## Conclusion

This article demonstrates that it is possible to run large language models on limited hardware. Placement laws and optimization strategies are critical for enhancing performance.

## Significance

This work makes large language models more accessible. Running these models on home computers could significantly contribute to the widespread adoption of AI applications.

## Sources covering this story

- https://github.com/FedericoTs/quantprobe — 

---

_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._