The rapid evolution of large language models (LLMs) has led to breakthroughs in many AI applications, from text generation to problem-solving across various industries. However, these models, such as GPT-4 and BERT, are often gigantic, with billions of parameters, making them difficult to deploy efficiently without significant computational resources. To address this, open-source communities and researchers have developed techniques to compress LLMs, enabling broader usage without sacrificing much of their capability.
What Is Compression in LLMs?
Compression in LLMs refers to techniques used to reduce the model’s size, making it more accessible for deployment on smaller hardware, including personal devices or in low-resource environments. Compression typically involves reducing the number of parameters, lowering memory requirements, and increasing inference speeds, all while aiming to retain the model’s performance.
Several methods are commonly used to compress LLMs:
- Quantization
Quantization reduces the precision of the numbers used to represent model parameters. For example, instead of using 32-bit floating-point numbers to store weights, the model can be compressed to use 16-bit, 8-bit, or even 4-bit representations. This drastically reduces memory usage and can speed up inference. - Pruning
Pruning removes unnecessary or less important neurons and connections from the model. After the model is trained, parameters that have little effect on the output can be eliminated, reducing the model’s size and complexity. Pruning can be done statically (once) or dynamically (as the model operates). - Knowledge Distillation
Knowledge distillation involves training a smaller model (student model) to mimic the behavior of a larger model (teacher model). The larger model is used to generate outputs or predictions, which are then used to train the smaller one, effectively “distilling” the knowledge of the large model into a smaller form. - Weight Sharing
In weight sharing, groups of weights within the model are forced to share the same value, reducing the number of unique weights stored. This approach decreases the model’s memory footprint without significantly degrading performance. - Low-Rank Factorization
This technique decomposes large matrices within the model into smaller ones, which can be multiplied to reconstruct the original matrix during inference. This reduces the memory required to store the matrices and can accelerate computations. - Parameter-Efficient Fine-Tuning (PEFT)
Instead of fine-tuning the entire model, which can be computationally expensive, PEFT focuses on adjusting only a small subset of parameters while keeping the rest of the model frozen. This reduces the size of the additional parameters needed to adapt a model to a new task.
How Compressed Are LLMs by Default?
To understand the degree of compression, it’s essential to compare the size of compressed models with the original training data and model parameters. LLMs, especially those like GPT-3 and GPT-4, are trained on vast datasets that amount to hundreds of gigabytes or even terabytes of text data. For example, GPT-3 was trained on over 570 GB of text data, but the model itself requires hundreds of gigabytes of storage depending on the number of parameters.
When models are compressed using techniques like quantization or pruning, the size can be reduced drastically. Here’s a rough comparison between uncompressed and compressed models:
| Model | Size (Uncompressed) | Size (Compressed) (Quantized/Pruned) | Compression Ratio |
|---|---|---|---|
| GPT-3 (175 billion params) | ~350 GB | ~40 GB (8-bit quantization) | 8.75:1 |
| BERT Large (340 million params) | ~1.3 GB | ~300 MB (8-bit quantization) | 4.33:1 |
| GPT-2 (1.5 billion params) | ~6 GB | ~1 GB (8-bit quantization) | 6:1 |
In general, compression techniques can reduce the size of LLMs by a factor of 4x to 10x, depending on the method used. However, quantization can achieve more aggressive compression, while maintaining a reasonable trade-off between size and performance.
Compression vs. Original Training Data
When compared to the original training data size, LLMs in their compressed forms are significantly smaller. For example, while the training data for GPT-3 was around 570 GB, the model, even in its full size with 175 billion parameters, is much smaller (~350 GB). After compression, it can shrink to as little as 40 GB, which is nearly 14 times smaller than the training data.
This indicates that even though the model learns from vast amounts of text, it does not store the data directly. Instead, it encodes the relationships between words and contexts into its parameters. Compression further reduces this by cutting down the overhead of storing precise values for these parameters.
Why Compression Matters
Compression is crucial for making LLMs available for a wide range of applications, especially in open-source communities. Without compression, many of these models would remain inaccessible, limited to only those with high-end hardware. By compressing LLMs, open-source developers can deploy state-of-the-art AI tools for:
- Mobile applications
Compressed models can run on smartphones or edge devices, enabling AI-driven tools like voice assistants or translation apps. - Research and Development
Open-source AI communities benefit from compressed models, allowing researchers to experiment with LLMs without requiring massive computational resources. - Enterprise Solutions
Organizations can deploy compressed LLMs in low-resource environments, enabling intelligent automation, customer service solutions, and more without the need for extensive hardware.
Conclusion
In the open-source world, compressing LLMs is a game-changer, allowing these powerful models to be deployed in diverse environments, from mobile devices to research labs. By utilizing techniques like quantization, pruning, and knowledge distillation, we can drastically reduce the size of LLMs without sacrificing much of their performance. On average, compressed LLMs are 4 to 10 times smaller than their uncompressed counterparts, and when compared to the size of the original training data, they can be 10 to 14 times smaller.
The power of AI should be accessible to everyone, and compression techniques are making that a reality.
I, Evert-Jan Wagenaar, resident of the Philippines, have a warm heart for the country. The same applies to Artificial Intelligence (AI). I have extensive knowledge and the necessary skills to make the combination a great success. I offer myself as an external advisor to the government of the Philippines. Please contact me using the Contact form or email me directly at evert.wagenaar@gmail.com!