2Tuning Techniques for Cost Optimization
FINE‐TUNING AND CUSTOMIZABILITY
LLMs such as BERT, GPT‐3, and PaLM have achieved state‐of‐the‐art performance on a wide range of NLP tasks. However, the computational demands of training and deploying these massive models present significant challenges. LLMs rely on an enormous number of parameters, with models scaling up to hundreds of billions of parameters. For example, GPT‐3 contains 175 billion parameters, while PaLM reaches 540 billion parameters. This massive scale enables them to learn rich representations of language from huge datasets during pre‐training. However, it also leads to immense computational requirements.
Specifically, the memory footprint and floating‐point operations (FLOPs) scale linearly with the number of parameters. This makes training and fine‐tuning computationally infeasible without specialized hardware. Even the deployment of such models for inference can be prohibitively expensive. As models continue to grow in scale, these costs will only increase. For example, training GPT‐3 was estimated to cost $12 million! Furthermore, full fine‐tuning requires training a separate model instance for each new task, which is storage‐intensive. The computational barriers restrict the wider adoption of LLMs and preclude many organizations from benefiting from their ...
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