QuantX: A Framework for Hardware-Aware Quantization of Generative AI Workloads (2505.07531v2)

Abstract: We present QuantX: a tailored suite of recipes for LLM and VLM quantization. It is capable of quantizing down to 3-bit resolutions with minimal loss in performance. The quantization strategies in QuantX take into account hardware-specific constraints to achieve efficient dequantization during inference ensuring flexible trade-off between runtime speed, memory requirement and model accuracy. Our results demonstrate that QuantX achieves performance within 6% of the unquantized model for LlaVa-v1.6 quantized down to 3-bits for multiple end user tasks and outperforms recently published state-of-the-art quantization techniques. We further integrate one particular technique from QuantX into the popular Llama.cpp framework and show its feasibility in terms of runtime compared to the mainstream quantization techniques from Llama.cpp. Lastly, this manuscript provides insights into the LLM quantization process that motivated the range of recipes and options that are incorporated in QuantX.

• The paper introduces QuantX, a framework that applies hardware-aware post-training quantization to achieve nearly unquantized performance with a 3-bit model resolution.
• It employs a hybrid quantization approach by analyzing weight distribution differences and mitigating outlier impacts to optimize both self-attention and MLP components.
• The framework integrates hardware constraints by supporting efficient kernel usage and numeral formats, ensuring practical deployment on resource-limited devices.

QuantX: A Framework for Hardware-Aware Quantization of Generative AI Workloads

Introduction

QuantX represents a significant stride in the development of quantization frameworks specifically tailored for LLMs and VLMs, addressing the prevalent need for efficient inference on edge and mobile devices where resources are typically constrained. By focusing on post-training quantization, the work aims to achieve highly memory-efficient models without substantial degradation in performance, which is crucial for maintaining functionality in privacy-sensitive applications where local inference is mandated.

QuantX Framework

The QuantX framework introduces a series of quantization techniques informed by empirical observations of weight distribution patterns in LLMs and VLMs. These insights allow for the creation of quantization recipes that consider the probability density functions of weight matrices, the influence of critical outlier weights, and the varying impact of quantization on attention and MLP components of the models.

Understanding PDF Differences:

The framework analyzes variations in the weight distribution across different layers and modules, recognizing that such variations necessitate distinct quantization approaches for optimal model performance.

Figure 1: Normalized histograms of the weight matrix $\mathbf{Q}$ from different model components highlight variation in distribution.

Critical Outliers:

The paper identifies that non-uniform quantization strategies may not always be superior due to the presence of outliers that induce large quantization errors. This understanding shapes the hybrid quantization strategies in QuantX.

Multi-Criterion Approach:

Beyond Frobenius norm error minimization, QuantX leverages criteria like self-attention output fidelity to guide quantization, ensuring preservation of essential model behaviors even under aggressive bit-width reductions.

Figure 2: Group-specific weight distributions and codebook mapping illustrate quantization strategies and highlight outliers.

Hardware-Aware Constraints

The recognition of hardware limitations informs the design of QuantX, ensuring practical utility of quantized models through compatible deployment strategies. The framework emphasizes adherence to real-world constraints like supported numeral formats and efficient kernel usage to avoid trade-offs that could undermine the advantages gained through quantization.

Results

Performance Outcomes:

QuantX demonstrates its capability to nearly match unquantized model performance with substantially reduced memory footprint, achieving within 6% of the original accuracy for LLMs like LlaVa-v1.6 when condensed to a 3-bit resolution.

Runtime Integration:

The incorporation of QuantX into platforms such as Llama.cpp underscores its practical applicability, showcasing improved token throughput and reduced file sizes compared to pre-existing techniques like Q40 and Q4K.

Figure 3: Functional overview of Q4X demonstrating reduction in dimensional overhead via learned histogram coding.

Conclusion

QuantX provides a framework that adeptly balances the competing demands of model fidelity, resource efficiency, and hardware compatibility, significantly enhancing the deployment of generative AI models in constrained environments. Its strategic approach to quantization leverages detailed analytical insights, guiding the design of versatile quantization techniques suitable for a variety of applications. The ongoing evolution of QuantX promises to facilitate further advancements in efficient AI model deployment, particularly as new challenges and hardware capabilities arise.