Performance Trade-offs of Optimizing Small Language Models for
  E-Commerce
Authors: name: Josip Tomo Licardo, name: Nikola Tankovic

Large Language Models (LLMs) offer state-of-the-art performance in natural
language understanding and generation tasks. However, the deployment of leading
commercial models for specialized tasks, such as e-commerce, is often hindered
by high computational costs, latency, and operational expenses. This paper
investigates the viability of smaller, open-weight models as a
resource-efficient alternative. We present a methodology for optimizing a
one-billion-parameter Llama 3.2 model for multilingual e-commerce intent
recognition. The model was fine-tuned using Quantized Low-Rank Adaptation
(QLoRA) on a synthetically generated dataset designed to mimic real-world user
queries. Subsequently, we applied post-training quantization techniques,
creating GPU-optimized (GPTQ) and CPU-optimized (GGUF) versions. Our results
demonstrate that the specialized 1B model achieves 99% accuracy, matching the
performance of the significantly larger GPT-4.1 model. A detailed performance
analysis revealed critical, hardware-dependent trade-offs: while 4-bit GPTQ
reduced VRAM usage by 41%, it paradoxically slowed inference by 82% on an older
GPU architecture (NVIDIA T4) due to dequantization overhead. Conversely, GGUF
formats on a CPU achieved a speedup of up to 18x in inference throughput and a
reduction of over 90% in RAM consumption compared to the FP16 baseline. We
conclude that small, properly optimized open-weight models are not just a
viable but a more suitable alternative for domain-specific applications,
offering state-of-the-art accuracy at a fraction of the computational cost.