3DXPoint memory is the first commercially available NVM solution targeting mainstream computer systems. While 3DXPoint conforms to many assumptions about NVM in previous studies, we observe a number of distinctive features of 3DXPoint. For example, the number of modified words in a cache line does not affect the performance of 3DXPoint writes. This enables a new type of optimization: performing more NVM word writes per line in order to reduce the number of NVM line writes. We propose LB + -Tree, a persistent B + -Tree index optimized for 3DXPoint memory. LB + -Tree nodes are 256B or a multiple of 256B, as 256B is the internal data access size in 3DXPoint memory. We propose three techniques to improve LB + -Tree's insertion performance: (i) Entry moving, which reduces the number of NVM line writes for insertions by creating empty slots in the first line of a leaf node; (ii) Logless node split, which uses NAW (NVM Atomic Write) to reduce logging overhead; and (iii) Distributed headers, which makes (i) and (ii) effective for multi-256B nodes. Theoretical analysis shows that entry moving reduces the number of NVM line writes per insertion of the traditional design by at least 1.35x in a stable tree. Our micro-benchmark experiments on a real machine equipped with 3DXPoint memory shows that LB + -Tree achieves up to 1.12--2.92x speedups over state-of-the-art NVM optimized B + -Trees for insertions while obtaining similar search and deletion performance. Moreover, we study the benefits of LB + -Tree in two real-world systems: X-Engine, a commercial OLTP storage engine, and Memcached, an open source key-value store. X-Engine and Memcached results confirm our findings in the micro-benchmarks.