UniSVQ: 2-bit Unified Scalar-Vector Quantization

Recent advances in deep learning have led to the development of extremely large models, such as GPT and Llama series, which achieve remarkable performance but require enormous computational and storage resources. To address these challenges, model compression techniques like pruning, distillation, and quantization have been extensively studied. Quantization reduces the bit-width of weights and activations, enabling faster inference and lower memory footprint. Early quantization methods, such as uniform scalar quantization, perform well at 4 bits or higher but degrade significantly at 2 bits. Vector quantization offers better performance at low bits by mapping weight groups to codebook vectors, but introduces complexity in storage and decoding. Recent research explores hybrid approaches, including structured and constrained quantization, to balance performance and efficiency. Despite progress, achieving high accuracy at 2-bit remains challenging, especially for large models, motivating the development of novel frameworks like UniSVQ.

The core challenge lies in maintaining model accuracy under extremely low-bit quantization, particularly at 2 bits. Scalar quantization suffers from performance drops due to its inability to adapt to weight distribution and sensitivity to outliers. Vector quantization, while more flexible, incurs high storage costs and decoding complexity, especially when codebooks are large or unstructured. Existing methods often face a trade-off between efficiency and accuracy, limiting their deployment in real-world scenarios. Additionally, the lack of hardware-compatible, high-performance quantization schemes hampers practical adoption. Overcoming these limitations requires a new approach that combines the strengths of both methods while minimizing overhead.

UniSVQ introduces a unified quantization framework that parameterizes codewords as an affine transformation of integer lattices, bridging scalar and vector quantization. The key innovation is the linear-constrained quantization grid, which retains the structure of scalar quantization while allowing the flexibility of vector schemes. Randomized Hadamard transforms preprocess weights to reduce outliers, and LDL decomposition enables layer-wise fine-tuning of the affine parameters, minimizing quantization errors. This approach maintains compatibility with optimized integer kernels, reduces storage requirements, and enhances performance at 2 bits. Unlike prior methods that rely on unstructured codebooks or complex non-linear transforms, UniSVQ offers a simple, hardware-friendly, and highly effective solution.

• �� Preprocessing: Apply randomized Hadamard transform (RHT) to weights, redistributing outliers and ensuring uniformity.
• �� Grid construction: Initialize affine transformation parameters using a random orthogonal matrix G, setting up a structured, linear-constrained quantization grid.
• �� Quantization: Use LDL-based LDLQ method to perform layer-wise weight quantization, adjusting for errors via LDL decomposition.
• �� Fine-tuning: Treat affine parameters as trainable, optimize via backpropagation using layer-wise MSE loss, using activations and original outputs as targets.
• �� Implementation: Integrate the process into existing inference pipelines, maintaining hardware compatibility and efficiency.

The evaluation involved models from Qwen-3 and Llama-3 families, ranging from 4B to 32B parameters. Using 1024 sequences from the RedPajama dataset, the LDLQ calibration was performed, and the models were quantized at 2 bits. The experiments compared UniSVQ with baseline scalar methods (GPTQ, OSTQuant) and vector methods (AQLM, Quip#). Performance metrics included perplexity (PPL) and zero-shot accuracy across multiple NLP benchmarks. Fine-tuning lasted about 6 hours on Nvidia A100 GPUs, with hyperparameters d=4, learning rate 5e-5, and early stopping at 3 epochs. The results demonstrated the robustness and effectiveness of UniSVQ across tasks and model sizes.

UniSVQ consistently outperformed traditional scalar quantization, reducing PPL by over 30% and maintaining over 98% of FP16 accuracy at 2 bits. It matched or exceeded the performance of vector quantization methods, with a 20% increase in inference throughput. Ablation studies confirmed the importance of the affine fine-tuning step, and the robustness across different model scales and tasks was validated. The results highlight the method’s ability to balance performance, efficiency, and hardware compatibility, making it suitable for practical deployment.

The framework can be applied in edge devices, cloud inference, and real-time AI services, enabling low-cost deployment of large models. It is particularly useful for scenarios requiring high throughput and low latency, such as conversational AI, real-time translation, and embedded systems. The approach also facilitates model compression in resource-constrained environments, broadening AI accessibility and reducing operational costs. Future integration with hardware accelerators could further enhance its impact.

The layer-wise fine-tuning process is computationally intensive, taking hours for large models, which may limit scalability. The method’s performance depends on the initial parameterization, and suboptimal choices can degrade accuracy. Its effectiveness on non-Gaussian or highly skewed weight distributions remains uncertain, requiring further adaptation. Additionally, extending the approach to activation quantization and multi-bit schemes presents ongoing challenges.