You are shipping a generative AI feature that runs on edge devices with limited compute, memory, and battery. Larger models produce better outputs, but they are slower and less reliable under device constraints. You need an approach for deciding when to compress, distill, quantize, or offload, without degrading answer quality too far.
How would you optimize inference latency versus model accuracy for a generative AI model deployed on edge devices?
You are shipping a generative AI feature that runs on edge devices with limited compute, memory, and battery. Larger models produce better outputs, but they are slower and less reliable under device constraints. You need an approach for deciding when to compress, distill, quantize, or offload, without degrading answer quality too far.
How would you optimize inference latency versus model accuracy for a generative AI model deployed on edge devices?