QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy

QuBLAST is a new post-training quantization method that compresses large language models by 40-45% while maintaining performance, using block-level mixed-precision quantization and activation scaling to address computational and memory constraints in LLM deployment.

QuBLAST addresses a critical bottleneck in large language model deployment: the computational and memory overhead that prevents efficient edge device integration. While previous quantization approaches apply uniform compression across all network layers, this research introduces a more sophisticated alternative by treating different attention blocks with varying quantization levels based on their sensitivity to precision loss. This heterogeneous approach recognizes that not all layers equally impact model performance, enabling more aggressive compression in less critical sections.

The framework's activation scaling strategy tackles a specific technical challenge that has plagued quantization efforts: outlier activations that distort quantized values. Rather than deploying computationally expensive mitigation techniques, QuBLAST uses scaling maps to control activation ranges, improving quantization efficiency without introducing significant computational overhead. The methodology demonstrates broad applicability across diverse architectures including transformer-based models (Qwen, Llama, Mistral, Falcon) and emerging state-space models.

The experimental results carry substantial practical implications. Achieving 40-45% size reduction with only 5% perplexity degradation represents a favorable trade-off for resource-constrained environments like mobile devices, edge servers, and IoT systems. This enables organizations to deploy state-of-the-art language models locally without relying on cloud infrastructure, improving latency, privacy, and operational costs.

For the AI infrastructure sector, this represents progress toward democratizing LLM deployment beyond well-capitalized entities. Future development should focus on evaluating QuBLAST against quantization-aware training methods and testing on increasingly sophisticated model architectures as the field continues evolving.

• →QuBLAST achieves 40-45% model size reduction across multiple LLM architectures with only 5% performance degradation
• →Block-level mixed-precision quantization adapts compression intensity based on layer sensitivity rather than applying uniform quantization
• →The activation scaling strategy efficiently mitigates outlier impacts without expensive computational operations
• →Framework successfully handles both conventional transformer architectures and emerging state-space models
• →Results enable practical LLM deployment on resource-constrained devices and edge systems