Sign language translation (SLT) can help bridge the communication gap between the Deaf and hard-of-hearing community and the general population. Yet progress has been driven largely by a handful of high-resource sign languages, such as German or American Sign Language, supported by large annotated corpora and large computing budgets. Many sign languages, including Bangla Sign Language (BdSL), remain underrepresented because annotated data is scarce and much research assumes access to powerful hardware. In this paper, we share an early-stage adaptation of the gloss-free SpaMo framework for Bangla, called SpaMo-BdSL, and reflect on what we learned while building it under real resource limits. We report two findings that we believe matter for low-resource and underrepresented SLT. First, a small language-specific model (BanglaT5, 247M parameters) matches the translation quality of a much larger multilingual model (mT0-XL, 3.7B parameters) on our Bangla dataset, while being far cheaper to run; for example, it translates the test set in 40 seconds versus 2 minutes 43 seconds for mT0-XL. Second, a set of simple training changes suggested by a large language model (Claude Opus 4.6) allowed us to train the whole system on a single consumer-grade GPU. We argue that the common assumption ”bigger and more multilingual is better” can quietly exclude underrepresented sign languages and the researchers who work on them, and we use our experience to open a discussion on more inclusive, multilingual, and culturally aware SLT.