Large language models (LLMs) are revolutionizing how we interact with technology, but their sheer size makes them computationally expensive. To address this, researchers have developed various inference acceleration strategies like quantization and pruning. These methods make LLMs faster and more efficient, but do they come at a cost? New research explores the complex relationship between inference acceleration and bias in LLMs, revealing a nuanced and sometimes unpredictable impact. While some acceleration techniques like key-value cache quantization seem to have minimal effects on bias, others, such as activation-aware weight quantization, can actually exacerbate existing biases, particularly in sensitive areas like decision-making scenarios. This research highlights the importance of carefully evaluating the impact of acceleration techniques not just on performance, but also on fairness and ethical considerations. The findings suggest that a one-size-fits-all approach won't work—each model and acceleration strategy needs to be assessed individually to ensure responsible and unbiased AI deployment. The study also emphasizes the importance of ongoing research into mitigating bias in LLMs, suggesting future model training should anticipate the use of acceleration strategies to minimize potential downstream biases. Ultimately, balancing performance with fairness remains a critical challenge in the ongoing development of large language models.