Have you ever wondered why you can’t run powerful AI models like ChatGPT on your own computer? These Large Language Models (LLMs) typically need expensive, power-hungry hardware. But what if you could run them on your regular laptop without needing fancy graphics cards?
A new AI compression approach called HIGGS is changing the game. Scientists from Yandex, MIT, KAUST, and ISTA have developed a clever approach called HIGGS that shrinks massive AI models to fit on everyday devices – without losing much of their smarts.
Table of Contents
Meet HIGGS
LLMs are amazing. They can write essays, code apps, answer deep questions—you name it. But they’re huge. Traditionally, you needed expensive GPUs and tons of memory to run them. To make them lighter, researchers have been using something called quantization, which shrinks the model by reducing the number of bits used to store each weight.
But here’s the catch: most quantization methods are slow, complicated, and still need powerful hardware to even begin. Now, enter HIGGS, which flips that whole process on its head. HIGGS stands for Hadamard Incoherence with Gaussian MSE-Optimal Grids.
It makes LLMs smaller without sacrificing their brainpower. Unlike traditional methods, this method doesn’t need calibration data or lots of tweaking. You just take a model, apply a special trick, and boom: your LLM is ready to roll on a laptop or even a smartphone.
How HIGGS Compresses LLMs
It takes a unique approach to quantization:
- It uses something called “Hadamard rotations” to reorganize the model’s internal numbers. Hadamard transformations make the model’s weight distribution approximately Gaussian (bell-curved), which is ideal for compression
- Then, it applies special grids called MSE-optimal grids. These minimize the mathematical error when compressing numbers.
- Vector quantization allows compressing groups of values together.
- Dynamic programming finds the optimal compression for each layer.
- All this happens without needing any example data (what experts call “data-free”).
What’s cool is that HIGGS doesn’t need powerful hardware. You can run it right on your laptop, and it only takes minutes instead of hours or days.
These techniques work together to create a compression pipeline that’s both very effective and computationally efficient.
The Science Behind HIGGS
The breakthrough that makes HIGGS possible is something the researchers call the “linearity theorem.” This mathematical insight shows exactly how making changes to different parts of the model affects its overall performance.
Each part of an AI model contributes differently to the final result. The linearity theorem helps identify exactly how sensitive each part is, so you can be more careful with the important bits and compress other parts more aggressively.
This theory works especially well for models compressed to between 3-8 bits per parameter (full-sized models typically use 16 bits). Below 3 bits, things get trickier, but that’s still quite impressive.
How AI Models Run Faster with HIGGS
Beyond just making models smaller, HIGGS also helps them run faster. The team created special software (called “kernels”) that takes advantage of how HIGGS compresses models. These kernels, based on something called FLUTE, can make models run 2-3 times faster than their original versions without significantly decreasing accuracy. This is particularly important for running these models on devices with limited processing power.
Dynamic HIGGS
Not all parts of an AI model are equally important. Some layers can be compressed more than others without affecting performance much. Dynamic HIGGS takes advantage of this.
Instead of compressing the entire model uniformly, Dynamic HIGGS can automatically determine the optimal compression level for each part of the model. This creates a non-uniform compression that gets better results than trying to compress everything equally.
The most impressive part? This optimization can be done without any training data using a technique they call “data-free dynamic quantization.”
Performance Evaluation of HIGGS
1. HIGGS vs. Other Compression Methods
The researchers compared this method to popular methods like Normal Float (NF), Abnormal Float (AF), and HQQ. HIGGS consistently performed better, especially when compressing models to very small sizes.
For example, when compressing the Llama 3.1 8B model, HIGGS maintained better accuracy on language understanding tasks than other methods. This was true across different models from the Llama and Qwen families.
What’s really interesting is that HIGGS sometimes even beat methods that require special training data, like GPTQ and AWQ. That’s impressive for a data-free approach!
2. Real-World Performance of HIGGS
The researchers tested HIGGS on several popular models, including Llama 3.1 8B, Llama 3.2 1B and 3B, Llama 3.1 70B and Qwen2.5 7B.
On tests like WikiText-2 (which measures how well the model predicts text) and reasoning tasks like ARC, PiQA, and HellaSwag, HIGGS consistently outperformed other compression methods.
For instance, when compressed to around 4 bits per parameter (a 4x reduction from the original size), HIGGS-compressed models barely lost any performance compared to the original 16-bit models.
Getting HIGGS Working
The HIGGS method can be implemented in popular AI frameworks like PyTorch and acceleration libraries like vLLM. This means developers can easily integrate it into existing systems.
Different configurations of HIGGS offer various tradeoffs between compression ratio, speed, and accuracy. For instance:
- p=1 configurations are simpler but less efficient
- p=2 configurations work well with FLUTE kernels for speed
- p=4 configurations offer better accuracy but may be slower
This flexibility lets developers choose the right balance for their specific application.
Key Benefits of HIGGS
The implications of this research are huge. Today, the most powerful AI models run in the cloud, on massive servers. This requires a constant internet connection, introduces privacy concerns, and creates a dependency on AI companies.
With HIGGS, the future could look different:
- Run advanced AI models directly on your laptop or phone
- Use AI assistants without an internet connection
- Keep your data private by processing it locally
- Reduce energy consumption and carbon footprint of AI
While the current research focuses on language models, the principles behind HIGGS could potentially apply to other types of AI models, too. This could lead to running advanced AI capabilities like image, video, speech and 3d model generation, speech recognition, translation, video analysis and more. All locally on consumer devices rather than requiring cloud servers.
Wrapping Up
By establishing a direct mathematical relationship between layer-wise errors and model performance, the researchers created a principled way to compress models. This isn’t just a practical hack – it’s based on solid mathematical theory about how neural networks work. That’s why HIGGS can achieve better results than methods that lack this theoretical grounding.
And the most interesting part? HIGGS has many advantages for users. It’s fast. It’s smart. It’s data-free. It works with existing tools. It can compress LLMs so well that you can run them on a regular laptop without breaking a sweat. And maybe best of all? HIGGS proves that innovation doesn’t have to come with a $10,000 GPU bill.
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