Transformer Optimization: Sparse Attention and LORA Techniques

Abstract

Transformer models have revolutionized natural language processing and computer vision through self-attention mechanisms. However, their quadratic computational complexity and massive parameter counts pose significant challenges for production deployment. This paper examines optimization techniques including sparse attention mechanisms, Low-Rank Adaptation (LoRA) fine-tuning, and quantization methods for efficient transformer deployment. We analyze sparse attention patterns including local attention, strided attention, and Longformer's sliding window mechanism, achieving O(n log n) complexity while maintaining competitive performance. LoRA reduces trainable parameters by 10,000× through low-rank decomposition, enabling efficient fine-tuning on consumer hardware. We evaluate quantization techniques including 8-bit and 4-bit weight compression, mixed-precision inference, and INT8 deployment strategies. Performance benchmarks demonstrate that optimized transformers achieve 3-10× inference speedup with minimal accuracy degradation. Our analysis provides practical guidelines for deploying large language models in resource-constrained production environments, balancing model quality, latency, and computational costs.