Performance Trade-offs of Optimizing Small Language Models for
  E-Commerce
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.
69050287b26ae8b17699fcc7
Josip Tomo Licardo
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2025-11-03T20:54:28.029Z
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Josip Tomo Licardo
jtlicardo
user
jtlicardo
69050287b26ae8b17699fcc8
Nikola Tankovic
Performance Trade-offs of Optimizing Small Language Models for
  E-Commerce
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.
Optimized smaller open-weight models achieve state-of-the-art performance in e-commerce intent recognition with reduced computational costs compared to larger models.
Quantized Low-Rank Adaptation (QLoRA) post-training quantization GPU-optimized (GPTQ) CPU-optimized (GGUF) VRAM usage inference throughput RAM consumption FP16 baseline