The proliferation of edge AI will require fundamental changes in language models and chip architectures to make inferencing and learning outside of AI data centers a viable option.
The initial goal for small language models (SLMs) — roughly 10 billion parameters or less, compared to more than a trillion parameters in the biggest LLMs — was to leverage them exclusively for inferencing. In...
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