Guide to Running GLM-4.5-Air on a 16GB RAM Machine
How to run large language models on limited hardware?
1Introduction
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.
2Hardware 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.
3Placement 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.
4Measurements 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.
5Sample 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.
6Performance 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.
7RAM Overclock
RAM overclocking provides a 52% performance boost in dense operations. This increase aligns with pre-registered predictions, further validating the laws.
8Hybrid 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.
9Innovative 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.
10Future 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.
11Conclusion
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.
12Significance
This work makes large language models more accessible. Running these models on home computers could significantly contribute to the widespread adoption of AI applications.
Original article: Hacker News: Show HN — GLM-4.5-Air Modelini 16GB RAM ile Çalıştırma Rehberi