Squeezing More From Less: A New Approach to Efficient AI Models

by

Author: Denis Avetisyan

Researchers have developed a novel method for compressing and adapting large language models, boosting performance and reducing computational demands.

LoRDS reimagines parameter-efficient fine-tuning by decomposing block-wise scaling into a low-rank product <span class="katex-eq" data-katex-display="false">W\odot B_{AW} </span>, enabling granular refinement via post-training quantization or direct fine-tuning with high-rank updates and-unlike methods like QLoRA which introduce additive, non-mergeable adapters-zero additional inference overhead through natural absorption into the dequantization process.
LoRDS reimagines parameter-efficient fine-tuning by decomposing block-wise scaling into a low-rank product W\odot B_{AW} , enabling granular refinement via post-training quantization or direct fine-tuning with high-rank updates and-unlike methods like QLoRA which introduce additive, non-mergeable adapters-zero additional inference overhead through natural absorption into the dequantization process.

LoRDS leverages low-rank decomposition of scaling factors for unified quantization and parameter-efficient fine-tuning of large language models.

Current large language model quantization techniques often sacrifice representational flexibility for computational efficiency via block-wise structures. The work ‘Breaking the Blocks: Continuous Low-Rank Decomposed Scaling for Unified LLM Quantization and Adaptation’ addresses this limitation by introducing Low-Rank Decomposed Scaling (LoRDS), a novel framework that models quantization scaling as a continuous low-rank manifold. This approach enables high-fidelity post-training quantization, efficient quantization-aware training, and high-rank parameter-efficient fine-tuning-all within a unified framework and without inference overhead. By “breaking the blocks” of traditional scaling, can LoRDS unlock a new paradigm for both compressing and adapting LLMs while maintaining-or even improving-performance?

The Inevitable Bottleneck: Resource Constraints in Large Language Models

The remarkable capabilities of large language models come at a significant cost: immense computational demands and substantial memory requirements. Training and even deploying these models often necessitates specialized hardware and considerable energy consumption, creating a barrier to entry for researchers and developers lacking access to such resources. This practical limitation restricts innovation and widespread adoption, preventing the full potential of these technologies from being realized by a broader community. The sheer scale of parameters – often billions – dictates the need for powerful processing units and large memory capacity, effectively concentrating access within well-funded institutions and corporations. Consequently, the democratization of artificial intelligence is hampered by the inherent resource intensity of current large language model architectures.

The remarkable capabilities of large language models come at a significant cost: full fine-tuning, the process of adapting a pre-trained model to a specific task, demands an extraordinary amount of computational power and memory. This isn’t simply a matter of needing faster hardware; the sheer scale of these models – often containing billions of parameters – means updating each of those parameters for a new application requires immense resources. Consequently, replicating the fine-tuning process, even for relatively modest tasks, can be prohibitively expensive and time-consuming, effectively limiting access to institutions and researchers with substantial computing infrastructure. The impracticality of full fine-tuning creates a crucial bottleneck, hindering wider adoption and preventing the democratization of advanced language AI capabilities.

Quantization presents a promising avenue for reducing the computational burden of large language models, but achieving this efficiency isn’t without challenges. This technique lowers the precision of the model’s weights and activations – for example, representing numbers with 8 bits instead of 32 – thereby shrinking its size and accelerating processing. However, this reduction in precision can introduce noticeable accuracy degradation. The core trade-off lies in determining the optimal level of quantization; aggressively reducing precision yields greater efficiency gains but risks substantial performance loss, while more conservative approaches may not deliver sufficient compression. Current research focuses on mitigating these accuracy trade-offs through techniques like quantization-aware training and mixed-precision quantization, striving to find the sweet spot where models remain both efficient and reliable.

The singular value distribution of weight updates for Llama3-8B’s first projection layer demonstrates that LoRDS, unlike QLoRA, achieves full-rank updates comparable to full fine-tuning via a multiplicative scaling approach.
The singular value distribution of weight updates for Llama3-8B’s first projection layer demonstrates that LoRDS, unlike QLoRA, achieves full-rank updates comparable to full fine-tuning via a multiplicative scaling approach.

Scaling Down: Parameter-Efficient Fine-Tuning as a Path Forward

Parameter-Efficient Fine-Tuning (PEFT) methods address the computational and storage limitations of full parameter fine-tuning for large language models. Traditional fine-tuning updates all model parameters-potentially billions-requiring substantial GPU memory and processing time. PEFT techniques, conversely, achieve comparable performance by only training a small number of newly introduced parameters – often less than 5% of the original model size. This is accomplished by freezing the pretrained model weights and injecting trainable layers, or adding low-rank adaptation matrices, into the existing architecture. Consequently, PEFT drastically reduces the computational cost and memory requirements, facilitating the adaptation of large models on resource-constrained hardware and enabling more efficient experimentation.

LoRA (Low-Rank Adaptation) modifies pretrained model weights by introducing trainable low-rank matrices alongside the original weights. This approach freezes the pretrained model parameters, preventing them from being updated during fine-tuning, and instead learns the changes necessary for a specific task via these smaller, added matrices. Specifically, for a weight matrix W_0 of dimension d \times k, LoRA introduces two smaller matrices A of dimension d \times r and B of dimension r \times k, where r \ll min(d, k). The updated weight matrix is then calculated as W_0 + BA. This significantly reduces the number of trainable parameters-from potentially billions in the original model to just a few million in the LoRA adaptation-while maintaining performance comparable to full fine-tuning.

QLoRA builds upon the Low-Rank Adaptation (LoRA) technique by incorporating 4-bit quantization to drastically reduce the memory requirements for fine-tuning large language models. This process quantizes the pretrained model weights to 4 bits, decreasing the memory footprint by approximately 75% compared to full 16-bit or 8-bit precision. LoRA then introduces trainable low-rank matrices which are added to the quantized weights during fine-tuning, allowing adaptation with a significantly smaller number of trainable parameters. This combination enables effective fine-tuning on consumer-grade hardware, such as a single GPU with 24GB of VRAM, that would otherwise be insufficient for training large models with traditional full parameter updates.

LoRDS demonstrates significantly lower operator latency compared to bitsandbytes NF4 and peft QLoRA across RTX 4090, RTX 5090, and H800 hardware, as measured by total processed tokens <span class="katex-eq" data-katex-display="false">MM</span>.
LoRDS demonstrates significantly lower operator latency compared to bitsandbytes NF4 and peft QLoRA across RTX 4090, RTX 5090, and H800 hardware, as measured by total processed tokens MM.

LoRDS: Reframing Quantization for Peak Efficiency

LoRDS addresses quantization scaling by representing the scaling matrix – traditionally a full-rank matrix – as a product of two low-rank matrices. This decomposition reduces the number of parameters required to represent the scaling, thereby decreasing memory footprint and computational cost. Instead of storing and processing a complete N \times N scaling matrix, LoRDS utilizes two matrices of size N \times R, where R << N. This low-rank approximation is achieved through techniques like Singular Value Decomposition (SVD) and allows for a more compact and efficient representation of the quantization parameters without significant loss of precision, as the low-rank structure captures the dominant factors influencing the scaling process.

The LoRDS framework enhances quantization parameter representation by decomposing the scaling matrix using techniques such as Singular Value Decomposition (SVD). Traditional quantization methods typically represent scaling with a full-rank matrix; however, LoRDS demonstrates that this matrix can be accurately and efficiently approximated with a low-rank product. This decomposition reduces the number of parameters required to represent the quantization scaling, lowering memory footprint and computational cost. Specifically, SVD identifies the principal components of the scaling matrix, allowing reconstruction using a significantly reduced set of singular values and vectors. This approach maintains quantization performance while enabling more flexible parameter adjustments and ultimately contributing to improved inference efficiency.

LoRDS achieves accelerated inference speeds through the implementation of custom kernels written in Triton, a language designed for high-performance GPU programming. This allows for optimized execution of quantized models on NVIDIA GPUs. Furthermore, LoRDS supports both post-training quantization (PTQ), which quantizes a pre-trained model without further training, and quantization-aware training (QAT), where quantization is incorporated into the training process to mitigate accuracy loss. This dual support provides flexibility for different deployment scenarios and allows users to select the quantization method best suited to their specific accuracy and performance requirements.

LoRDS demonstrates a significant performance improvement in inference speed when deployed on NVIDIA RTX 4090 GPUs. Benchmarking indicates LoRDS achieves up to a 1.5x speedup compared to the QLoRA quantization method. This performance gain is realized through the framework’s optimized implementation and efficient representation of quantization parameters, enabling faster computation during the inference process without substantial loss of accuracy. The observed speedup represents a measurable advancement in the efficiency of large language model deployment on high-end GPU hardware.

LoRDS employs a continuous low-rank manifold to represent quantization scaling factors, enabling precise control over the quantization process. Traditional quantization methods often discretize these scaling factors, leading to information loss and reduced model accuracy. By parameterizing the scaling matrix within a continuous low-rank space, LoRDS allows for gradient-based optimization of these factors during both post-training quantization and quantization-aware training. This approach effectively minimizes the reconstruction error between the original weights and the quantized weights, resulting in a more accurate and efficient quantized model. The low-rank decomposition reduces the number of trainable parameters associated with quantization, while the continuous nature of the manifold facilitates fine-grained adjustments that mitigate the impact of quantization on model performance.

Validating LoRDS: Preserving Reasoning in Compressed Models

Large language models often sacrifice reasoning ability when compressed for efficient deployment, but LoRDS demonstrably mitigates this performance loss. Through rigorous testing on commonsense reasoning tasks, LoRDS consistently achieves superior results when compared to leading quantization and parameter-efficient fine-tuning (PEFT) techniques. This preservation of reasoning capabilities stems from LoRDS’ innovative approach to low-rank adaptation, which allows the model to maintain crucial relationships between parameters even with significant compression. The method’s efficacy highlights a critical advancement in balancing model size and cognitive performance, paving the way for more accessible and powerful AI applications in resource-constrained settings.

Recent evaluations demonstrate that LoRDS, a novel parameter-efficient fine-tuning (PEFT) method, significantly enhances performance on the Llama3-8B model. Specifically, LoRDS achieves a notable 4.19% improvement in accuracy compared to the widely used QLoRA method during PEFT tasks. This gain suggests LoRDS more effectively preserves the crucial knowledge embedded within the large language model during the fine-tuning process, resulting in a model better equipped to handle complex reasoning and generation tasks. The improvement isn’t merely incremental; it indicates a substantial leap in the ability to adapt large models to specific applications while maintaining a high degree of fidelity to the original model’s capabilities.

LoRDS demonstrates a significant advancement in model compression by achieving a 35.7% reduction in quantization error when utilizing 2.25-bit quantization, a substantial improvement over the commonly used QLoRA method. This reduction in error is particularly noteworthy given the extreme level of compression; minimizing error at such low bit-widths is critical for maintaining model performance. The methodology employed within LoRDS allows it to preserve crucial information during the quantization process, resulting in a model that maintains a higher degree of accuracy despite its smaller size. This outcome suggests that LoRDS offers a pathway to deploy large language models on devices with limited computational resources without substantial performance degradation, marking a step forward in practical and efficient AI implementation.

Significant gains in model compression are demonstrated through LoRDS, specifically in minimizing information loss during the quantization process. At 3-bit quantization – a method of reducing a model’s size by representing its weights with fewer bits – LoRDS achieves a 31.8% reduction in quantization error compared to alternative techniques like QLoRA. This substantial decrease indicates LoRDS’ superior ability to retain critical information within the compressed model, preventing a significant drop in performance. By minimizing this error, LoRDS facilitates the creation of smaller, more efficient large language models without sacrificing accuracy, making advanced AI more accessible for deployment in environments with limited computational resources.

The LoRDS framework demonstrates a significant advancement in large language model performance, achieving an overall accuracy of 87.68% when applied to the Llama3-8B model. This result positions LoRDS as a leading method for preserving reasoning capabilities during model compression, notably exceeding the performance of alternative quantization and parameter-efficient fine-tuning techniques. Such a high level of accuracy, attained through innovations like the Hadamard product and block-wise quantization, indicates a practical pathway for deploying powerful language models even in resource-constrained settings without substantial performance degradation. The achievement highlights LoRDS’s capacity to effectively balance model size and analytical prowess, offering a compelling solution for a wider range of applications.

LoRDS leverages the Hadamard product – an element-wise multiplication of matrices – to streamline the computation of a low-rank approximation for the scaling matrix within large language models. This approach offers a significant efficiency gain because it avoids the computationally expensive matrix multiplication typically required for low-rank decomposition. By expressing the scaling matrix as a product of smaller, low-rank components via the Hadamard product, LoRDS reduces the number of parameters needing optimization during quantization and parameter-efficient fine-tuning (PEFT). This not only accelerates the compression and adaptation processes but also helps preserve the model’s reasoning capabilities, as the crucial scaling information is maintained with minimal loss – ultimately contributing to LoRDS’s superior performance on commonsense reasoning tasks compared to other quantization and PEFT methods.

LoRDS incorporates block-wise quantization as a refinement to its low-rank adaptation strategy, moving beyond individual weight quantization to process groups of weights collectively. This approach leverages the inherent correlations within weight matrices, enabling a more accurate representation of the original parameters with reduced precision. By quantizing blocks rather than single values, LoRDS minimizes information loss and significantly improves the preservation of critical features during compression. The result is a more efficient quantization process that not only reduces the model’s memory footprint but also maintains a higher level of performance on commonsense reasoning tasks, offering a substantial advantage over methods that quantize weights independently.

Large language models, while increasingly powerful, often demand substantial computational resources, hindering their deployment on edge devices or in real-time applications. LoRDS addresses this challenge by offering a novel approach to model compression that minimizes performance degradation. Through a combination of low-rank decomposition, Hadamard products, and block-wise quantization, LoRDS significantly reduces the memory footprint of these models without sacrificing their ability to reason and generate coherent text. This efficient compression allows for the practical deployment of sophisticated language capabilities in resource-constrained environments, opening doors for wider accessibility and innovative applications – from personalized on-device assistants to streamlined data analysis in remote locations.

The pursuit of efficient large language models, as demonstrated by LoRDS, inevitably introduces trade-offs. This work, focused on low-rank decomposition for quantization, exemplifies how simplification-reducing the precision of parameters-carries a future cost. As Alan Turing observed, “Sometimes people who are unhappy tend to look at the world as hostile.” This sentiment resonates with the challenges of model compression; aggressively reducing size can lead to performance degradation if not approached thoughtfully. LoRDS attempts to mitigate this ‘hostility’ by intelligently decomposing scaling factors, preserving crucial information while minimizing computational demands, acknowledging that even the most elegant solutions are subject to the inevitable decay inherent in complex systems. The framework’s success hinges on balancing compression with sustained accuracy, a delicate act reflective of the principle that technical debt is simply the system’s memory.

The Slow Gradient

The introduction of LoRDS, and its focus on decomposing scaling factors, represents a predictable, if useful, refinement. Every abstraction carries the weight of the past; the quest for efficient large language models will invariably devolve into increasingly intricate methods of compression and adaptation. The immediate gains in inference speed and parameter efficiency are noted, but the core challenge remains untouched: the inherent fragility of these massively parameterized systems. Time isn’t a metric to be ‘solved’ for, but the medium in which decay manifests.

Future work will undoubtedly explore the limits of these low-rank approximations, and likely introduce even more granular decomposition strategies. However, a truly resilient architecture will not merely strive for compactness. It will embrace a philosophy of gradual, controlled change-a system capable of self-repair and continuous recalibration. The current emphasis on squeezing performance from existing models postpones, but does not prevent, the inevitable need for fundamentally new approaches to learning and representation.

The question isn’t whether LoRDS, or its successors, will achieve ever-greater compression ratios. It’s whether the field can shift its focus from optimization to longevity. Only slow change preserves resilience, and the true measure of progress will be the ability to maintain functionality not over benchmarks, but across extended periods of operation and adaptation.

Original article: https://arxiv.org/pdf/2601.22716.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-02-03 04:42