Friday, July 14, 2023
Model optimization plays a critical role in the functioning of LLMs. It's a process that refines these models to be more compact, effectively decreasing their resource utilization.
Poorly optimized models require and consume considerable resources, i.e., CPU / GPU power, leading to higher costs and often resulting in slower performance.
The optimizations you'll perform depend on the model's objectives and the project's budget. That said, common strategies involve:
Model optimization is important for edge applications and server-side deployments alike, but they often have slightly different goals.
In edge applications, for example, real-time object and facial recognition in security camera monitoring at places like train stations, the focus of optimization will likely first lean towards reducing latency and improving alert accuracy.
On the other hand, server-side deployments, especially in enterprise contexts, can be expensive. Here, the optimization efforts center around cost reduction. The fewer resources required to run the model, the lower the overall costs. Therefore, optimization gains increased importance at this level.
A piece of advice when developing and testing your model is to initially prioritize functionality, ensuring the use case is working and demonstrable. Post-deployment, you can focus on optimization and continuous performance improvement.
What's the ultimate goal of optimization, the ideal, the panacea?
Creating a model that's so efficient it can outperform a GPU even on standard consumer hardware like an older laptop.
In conclusion, model optimization is a vital component in developing LLMs. Reducing resource usage and enhancing efficiency renders these models both cost-effective and performant.
