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A comprehensive empirical evaluation framework assesses efficiency techniques for Large Language Models across architecture pretraining, fine-tuning, and quantization dimensions, revealing key trade-offs between memory usage, compute utilization, latency, throughput and energy consumption while demonstrating effective transfer of findings to vision and multimodal models.