The Coupling from the Past (CFTP) paradigm is a canonical method for perfect sampling. For uniform sampling of proper q-colorings in graphs with maximum degree Δ, the bounding chains of [Huber, STOC '98] provide a systematic framework for efficiently implementing CFTP algorithms within the classical regime q ≥ (1+o(1))Δ². This was subsequently improved to q > 3Δ by [Bhandari and Chakraborty, STOC '20] and to q ≥ (8/3 + o(1))Δ by [Jain, Sah, and Sawhney, STOC '21]. In this work, we establish the asymptotically tight threshold for bounding-chain-based CFTP algorithms for graph colorings. We prove a lower bound showing that all such algorithms satisfying the standard contraction property require q ≥ 2.5Δ, and we present an efficient CFTP algorithm that achieves this asymptotically optimal threshold q ≥ (2.5 + o(1))Δ via an optimal design of bounding chains.