  In recent years, multilingual machine translation models have achieved
promising performance on low-resource language pairs by sharing information
between similar languages, thus enabling zero-shot translation. To overcome the
"curse of multilinguality", these models often opt for scaling up the number of
parameters, which makes their use in resource-constrained environments
challenging. We introduce SMaLL-100, a distilled version of the M2M-100 (12B)
model, a massively multilingual machine translation model covering 100
languages. We train SMaLL-100 with uniform sampling across all language pairs
and therefore focus on preserving the performance of low-resource languages. We
evaluate SMaLL-100 on different low-resource benchmarks: FLORES-101, Tatoeba,
and TICO-19 and demonstrate that it outperforms previous massively multilingual
models of comparable sizes (200-600M) while improving inference latency and
memory usage. Additionally, our model achieves comparable results to M2M-100
(1.2B), while being 3.6x smaller and 4.3x faster at inference. Code and
pre-trained models: https://github.com/alirezamshi/small100
