Benchmarking Local Large Language Models for Structured Knowledge Extraction in Conversational Commerce

The integration of natural language understanding into conversational commerce requires robustly extracting structured intents from idiomatic text. While cloud-based models are highly capable, their latency, privacy risks, and operational costs necessitate efficient local alternatives. To address this, we empirically evaluate locally deployed, open-weights Large Language Models (LLMs) on parsing multi-intent shopping cart commands into deterministic JSON schemas. We benchmark multiple models across minimal, extended, and few-shot prompting paradigms, utilizing a dual-axis framework to simultaneously measure extraction quality (operation F1-score, exact match, schema validity) and hardware performance (latency, GPU power consumption). Our evaluation highlights a fundamental trade-off between schema compliance and computational overhead, demonstrating how advanced prompting techniques improve parsing accuracy while measurably impacting inference latency and power draw. Ultimately, this comprehensive profiling identifies optimal model configurations for resource-constrained environments: a well-chosen 3-8B parameter model (specifically Granite4 3B) paired with few-shot prompting recovers over 95% of the quality of the largest evaluated model at a coarse indicative estimate of 40× lower operational energy per command, establishing a practical baseline for deploying cost-effective LLMs as efficiency candidates in resource-constrained environments.