#inference acceleration

CTranslate2 facilitates efficient Transformer model inference with techniques like weight quantization and layer fusion, optimizing memory usage on both CPU and GPU. Supporting encoder-decoder, decoder-only, and encoder-only model types, it integrates seamlessly with multiple frameworks. Features include fast execution, reduced precision support, and minimal storage needs. The library's capabilities, such as automatic CPU selection, parallel processing, and ease of use in Python and C++, make it a reliable option for production use, distinctly outperforming general-purpose deep learning frameworks.

DeepSparse is an inference runtime that utilizes model sparsity to enhance neural network performance on CPUs. It works with SparseML for model pruning and quantization, improving inference speed and efficiency across various models such as LLMs, CNNs, and Transformers. The latest Sparse Fine-Tuning advancements enable up to 60% sparsity in models like MPT-7B without sacrificing accuracy, boosting performance significantly. It offers APIs including Engine, Pipeline, and Server for flexible integration and deployment, making it suitable for high-efficiency AI applications.

Explore Sparsify, a tool that accelerates machine learning model inference using sparse optimization methods. Designed for efficiently fine-tuning large language models on CPUs, Sparsify provides a web application and CLI/API package for easy management and experimentation. Benefit from its ability to improve model efficiency through pruning, quantization, and distillation, while accessing comprehensive guides for setup, execution, and comparison. Sparsify offers a practical solution for developing more efficient AI models.