Inference Optimization 🏎️
Discover various techniques and strategies to optimize the inference speed and efficiency of Large Language Models.
Quantization
Reduce model size and increase efficiency by using lower precision data types like INT8 or FP16.
Model Pruning
Optimize model inference by removing unnecessary weights or neurons.
Hardware Acceleration
Leverage hardware accelerators like GPUs, TPUs, or dedicated AI chips to speed up inference.
Batching & Parallelism
Use batching and parallelism to process multiple inputs concurrently for faster inference.
Distillation
Reduce the size of a large LLM by transferring its knowledge to a smaller, faster model.
Caching Mechanisms
Implement caching techniques to store results of repeated computations for faster inference.
Optimized Kernels
Use optimized libraries like cuDNN, Intel MKL, and OpenVINO for faster matrix computations.
Cloud-based Optimization
Explore cloud-based inference optimization techniques like serverless computing and edge computing.