This report examines the role of local versus cloud computing in scientific computing as practicedin chemical engineering, environmental engineering, biotechnology and process technology, withspecific attention to scientific machine learning, modelling, simulation and inference workloadstypical of PhD level research. The analysis is based on a meta analysis of publications from 2025and 2026 extracted from arXiv and associated GitHub repositories, complemented with peerreviewed literature in computational chemistry, engineering simulation and related fields. Across this evidence base, a substantial body of papers explicitly reports experiments executed onsingle workstation systems equipped with consumer RTX GPUs and roughly 128 GB of hostmemory. The frequency of these disclosures is of the same order of magnitude as references tocloud based computation. This is notable because the broader technology narrative stronglypromotes cloud infrastructure, largely through large scale commercial messaging, while theresearch literature shows that modern local compute remains an efficient, routine and acceptedexperimental platform. Taken together, the evidence indicates that for a large majority of day to day scientific computingtasks in these domains, a modern local workstation, for example a system with an RTX 5090 classGPU, about 128 GB RAM, multi terabyte NVMe storage and a contemporary multi core desktopprocessor, is already sufficient to execute modelling, training, inference and simulation workflows. Cloud and cluster resources remain important for genuinely large scale computations. However,the literature suggests a common development pattern: models are often built and stabilised onlocal machines first, with larger infrastructure used later when additional scale is needed. Thisreflects the iterative nature of research, where working locally helps ensure that methods behavecorrectly before larger resources are applied.