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Large transformer-based models have realized state-of-the-art performance on lots of real-world tasks such as natural language processing and computer vision. However, with the increasing sensitivity of the data and tasks they handle, privacy has become a major concern during model deployment. In this work, we focus on private inference in two-party settings, where one party holds private inputs and the other holds the model. We introduce BumbleBee, a fast and communication-friendly two-party private transformer inference system. Our contributions are three-fold: First, we propose optimized protocols for matrix multiplication, which significantly reduce communication costs by 80% -- 90% compared to previous techniques. Secondly, we develop a methodology for constructing efficient protocols tailored to the non-linear activation functions employed in transformer models. The proposed activation protocols have realized a significant enhancement in processing speed, alongside a remarkable reduction in communication costs by 80% -- 95% compared with two prior methods. Lastly, we have performed extensive benchmarks on five transformer models. BumbleBee demonstrates its capability by evaluating the LLaMA-7B model, generating one token in approximately 8 minutes using CPUs. Our results further reveal that BumbleBee outperforms Iron (NeurIPS22) by over an order of magnitude and is three times faster than BOLT (Oakland24) with one-tenth communication.