the biggest hurdle in any enterprise AI application: cost to serve. AI is abnormally compute-intensive regardless of how it’s deployed, and the relationship between model size and the expense is a clear one. As parameter counts grow, training and inference alike demand more silicon, more power, and more downstream costs like maintenance. To put this in perspective, consider that each individual.
Performance Of course cost savings
on’t matter much if the resulting deployment can’t offer competitive performance. But the assumption that smaller models must perform worse than their big siblings is, thankfully, simply wrong. First, it’s important to understand that model performance doesn’t exist along a single dimension; for instance, a model’s ability to solve problems america phone number list within a single domain—
Conversely, for companies looking
to build models focused on a well-defined domain, such as knowledge retrieval, technical support, and answering customer questions—small models are often neck and neck with large ones. In fact, with the ai database right strategy, they can outperform them altogether. A number of models from the open source world, including our own XGen 7B—a model specifically trained on longer sequences of data, helping it with tasks like the summarization of large volumes when your sales reps engag of text, writing code, and predicting protein sequences—consistently exceed the performance of larger models by leveraging better pretraining and data curation strategies.
