Many prediction tasks evaluate a trained tree ensemble on groups of rows that share feature values: discrete-time survival models expand each patient into $G$ time steps, click-through-rate models score every item in a search session, and scenario analyses vary a few inputs while holding the rest fixed. Standard inference treats each row independently and ignores this input-side correlation. We present TreeWalker, which instantiates partial evaluation for grouped tree-ensemble inference: constant features are static, varying features dynamic. TreeWalker walks each tree once per group, partitions a row bitmask at varying splits, and skips empty subtrees via an unsplit shortcut. It reads standard LightGBM and XGBoost models; training is unchanged. We prove a structural work decomposition: per-tree work splits into the constant-projected subtree size $|T_c|$, $G$ leaf writes, and a predicate-mask provisioning cost $Q$. For the trace evaluator, per-row work approaches $(d_v+1)/(d+1)$ as $G \to \infty$, giving asymptotic speedup $1/(1-\barρ_G)$. On Intel, TreeWalker is ${\sim}3\times$ faster than a row-independent traversal at the reference model size ($T{=}500$, $L{=}8$; $G{=}16$ for SUPPORT/FLCHAIN and empirical groups for Expedia) and $7.7$--$9.1\times$ faster at $G{=}128$ on the survival datasets; on Arm, $2.5$--$2.9\times$ and $5.5$--$6.0\times$. On a scenario-analysis benchmark it is faster in all $16$ configurations on both machines. For f64 models, outputs match treelite GTIL up to tree-ordering roundoff; for f32 models, TreeWalker's f64 leaf accumulation is closer than native f32 to an exactly rounded reference on every audited row.

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