Large Language Models No Longer Require Powerful Servers
Scientists from Yandex, HSE University, MIT, KAUST, and ISTA have made a breakthrough in optimising LLMs. Yandex Research, in collaboration with leading science and technology universities, has developed a method for rapidly compressing large language models (LLMs) without compromising quality. Now, a smartphone or laptop is enough to work with LLMs—there's no need for expensive servers or high-powered GPUs.
This method enables faster testing and more efficient implementation of new neural network-based solutions, reducing both development time and costs. As a result, LLMs are more accessible not only to large corporations, but also to smaller companies, non-profit laboratories and institutes, as well as individual developers and researchers.
Previously, running a language model on a smartphone or laptop required quantising on an expensive server—a process that could take anywhere from a few hours to several weeks. Quantisation can now be performed directly on a smartphone or laptop in just a few minutes.
Challenges in implementing LLMs
The main obstacle to using LLMs is that they require considerable computational power. This applies to open-source models as well. For example, the popular DeepSeek-R1 is too large to run even on high-end servers built for AI and machine learning workloads, meaning that very few companies can effectively use LLMs, even if the model itself is publicly available.
The new method reduces the model's size while maintaining its quality, making it possible to run on more accessible devices. This method allows even larger models, such as DeepSeek-R1 with 671 billion parameters and Llama 4 Maverick with 400 billion parameters, to be compressed, which until now could only be quantised using basic methods and resulted in significant quality loss.
The new quantisation method opens up more opportunities to use LLMs across various fields, particularly in resource-limited sectors such as education and the social sphere. Startups and independent developers can now implement compressed models to create innovative products and services without the need for costly hardware investments. Yandex is already applying the new method for prototyping—creating working versions of products and quickly validating ideas. Testing compressed models takes less time than testing the original versions.
Key details of the new method
The new quantisation method is named HIGGS (Hadamard Incoherence with Gaussian MSE-Optimal GridS). It enables the compression of neural networks without the need for additional data or computationally intensive parameter optimisation. This is especially useful in situations where there is not enough relevant data available to train the model. HIGGS strikes a balance between the quality, size, and complexity of the quantised models, making them suitable for use on a variety of devices.
The method has already been validated on the widely used Llama 3 and Qwen2.5 models. Experiments have shown that HIGGS outperforms all existing data-free quantisation methods, including NF4 (4-bit NormalFloat) and HQQ (Half-Quadratic Quantisation), in terms of both quality and model size.
Scientists from HSE University, the Massachusetts Institute of Technology (MIT), the Austrian Institute of Science and Technology (ISTA), and King Abdullah University of Science and Technology (KAUST, Saudi Arabia), all contributed to the development of the method.
The HIGGS method is already accessible to developers and researchers on Hugging Face and GitHub, with a research paper available on arXiv.
Response from the academic community, and other methods
The paper describing the new method has been accepted for presentation at one of the largest AI conferences in the world—the North American Chapter of the Association for Computational Linguistics (NAACL). The conference will be held from April 29 to May 4, 2025, in Albuquerque, New Mexico, USA, and Yandex will be among the attendees, along with other companies and universities such as Google, Microsoft Research, and Harvard University. The paper has been cited by Red Hat AI, an American software company, as well as Peking University, Hong Kong University of Science and Technology, Fudan University, and others.
Previously, scientists from Yandex presented 12 studies focused on LLM quantisation. The company aims to make the application of LLMs more efficient, less energy-consuming, and accessible to all developers and researchers. For example, the Yandex Research team has previously developed methods for compressing LLMs, which reduce computational costs by nearly eight times, while not significantly compromising the quality of the neural network’s responses. The team has also developed a solution that allows running a model with 8 billion parameters on a regular computer or smartphone through a browser interface, even without major computational power.
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