We study what language models can learn when compute is scarce. Because the questions that matter shouldn't need a datacenter to ask.
SMALL MODELS. OPEN POSSIBILITIES.
Good ideas deserve a chance. Compute shouldn't decide which ones.
When access to compute determines who can experiment, it shapes which ideas get discovered. At nimblelab.ai, we see small-model research as a way to keep meaningful AI experimentation within reach of smaller labs, universities, and independent researchers.
Small models give us room to test ideas, investigate failures, and challenge assumptions before committing to expensive training runs.
Carefully designed experiments can reveal principles that inform how we build better models at larger scales.
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