What is Model Inference Optimization?
Model inference optimization focuses on improving the speed, efficiency, and resource utilization of AI models during deployment. The primary goals are:
- Reduce memory footprint by using fewer GPU devices and less GPU memory
- Lower computation complexity by reducing the number of FLOPs needed
- Decrease inference latency to make models run faster
- Maintain model performance while optimizing for efficiency
Optimization Techniques We Use
Quantization
Reduce model precision (FP32 to FP16 or INT8) to decrease model size and accelerate computations.
Pruning
Remove unnecessary model parameters and connections to reduce complexity without significant performance loss.
Knowledge Distillation
Train smaller, faster models that learn from larger teacher models, maintaining performance with reduced size.
Specialized Hardware
Leverage GPU optimization, TPUs, and inference frameworks like TensorRT, ONNX Runtime for maximum performance.
Benefits of Model Inference Optimization
Faster inference times
Reduced infrastructure costs
Lower memory requirements
Improved scalability
Better user experience
Cost-effective deployment