A researcher successfully trained a 3.8 billion parameter large language model (LLM) to achieve a compute efficiency of 0.384 CORE for just $998, demonstrating that high-performance models can be trained at a fraction of typical costs. This achievement highlights advances in efficient training techniques and cost-effective AI development. The model, referred to as "Little LM," challenges the prevailing assumption that large-scale models require massive budgets.