Intertemporal CGE models allow agents to respond fully to current and future policy shocks. This property is particularly important for trade policies, where tariff reductions span over decades. Nevertheless, intertemporal CGE models are dimensionally large and computationally difficult to solve, thus hindering their development, save for those that are scaled-down to only a few regions and commodities. Using a recently developed solution method, we address this problem by building an intertemporal version of a GTAP model that is large in dimension and can be easily scaled to focus to any subset of GTAP countries or regions, without the need for 'second best' recursive approaches. Specifically, we solve using a new parallel-processing technique and matrix reordering procedure, and employ a non-steady state baseline scenario. This provides an effective tool for the dynamic analysis of trade policies. As an application of the model, we simulate a free trade scenario for Vietnam with a focus on the recent Trans-Pacific Partnership (TPP). Our simulation shows that Vietnam gains considerably from the TPP, with 60 of the gains realised within the first 10 years despite our assumption of a gradual and linear removal of trade barriers. We also solve for intertemporal and sector-specific effects on each industry in Vietnam from the trade agreements, showing an added advantage of our approach compared to standard static and recursive GTAP models.