Meta’sLLaMAfamily has become one of the most powerful open-source Large Language Model (LLM)  series. Notably,LLaMA3models have recently been released and achieve impressive performance across various with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-limited scenarios, we exploreLLaMA3’s capabilities when quantized to low bit-width. This exploration holds the potential to unveil new insights and challenges for low-bit quantization ofLLaMA3and other forthcoming LLMs, especially in addressing performance degradation problems that suffer in LLM compression. Specifically, we evaluate the 10 existing post-training quantization and LoRA-finetuning methods ofLLaMA3on 1-8 bits and diverse datasets to comprehensively revealLLaMA3’s low-bit quantization performance. Our experiment results indicate thatLLaMA3still suffers non-negligent degradation in these scenarios, especially in ultra-low bit-width. This highlights the significant performance gap under low bit-width that needs to be bridged in future developments. We expect that this empirical study will prove valuable in advancing future models, pushing the LLMs to lower bit-width with higher accuracy for being practical. Our project is released onhttps://github.com/Macaronlin/LLaMA3-Quantizationand quantizedLLaMA3models are released inhttps://huggingface.co/LLMQ.