Quantized Transformers in Practice: Benchmarking Full- and Low-Precision LLMs across Two Processors

Quantization has emerged as an important technique for enabling efficient deployment of large language models (LLMs) by reducing their memory and computational requirements. This research conducts an evaluation of INT8 quantization on several state-of-the-art LLMs, GPT-2, LLaMA-2-7B-Chat and Qwen1.5-1.8B-Chat, across two hardware configurations: NVIDIA RTX4070 Laptop GPU and RTX4080 Laptop GPU and two tasks: text and code generation. By comparing quantized INT8 models with their FP16 counterparts and a human-written reference, we quantify the trade-offs between performance and efficiency using standard natural language generation metrics (BLEU, ROUGE-1, ROUGE-L) and semantic analysis via GPT-4o and Gemini 2.5 Flash (Google). The results reveal that INT8 post-training quantization (PTQ), hereafter referred to as INT8, substantially reduces inference time and memory footprint, with minimal impact on topical relevance but a notable decline in lexical precision, fluency and structural coherence. The extent of quality degradation varies by model size and architecture, with smaller models demonstrating greater resilience to quantization. Furthermore, we identify several limitations in quantized outputs, including reduced expressiveness, while highlighting their suitability for resource-constrained or real-time applications, such as robots monitoring safety standards in manufacturing environments. On average, INT8 quantization results in a 3.4 times speedup over FP16 inference across all tested models and GPUs (excluding configurations affected by CPU offloading), with the largest gains observed in large models like LLaMA-2-7B-Chat. The results also indicate that structured code generation exhibits slightly greater sensitivity to INT8 quantization compared to explanatory text generation.