[Python] How can deep learning models be optimized for real-time predictive text input in...

I'm working on a project that involves implementing a real-time predictive text input feature using deep learning models. The challenge I'm facing is that the target environment has limited computational resources, such as low processing power and memory constraints.

To address this, I've tried the following approaches:

I removed less significant neurons and layers from the model to reduce its size.

Converted the model weights from floating-point to lower precision (e.g., 8-bit integers) to reduce memory usage.

Despite these efforts, I'm still struggling to achieve the desired performance while maintaining low latency and accuracy.

Has anyone successfully optimized deep learning models for similar resource-constrained environments? What techniques or practices would you recommend to achieve real-time performance in such scenarios?

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