#sparsity

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.

Torchao provides effective solutions for PyTorch users to optimize inference and training through quantization and sparsity, enhancing model efficiency. It enables significant speed and memory improvements with weight and activation quantization. For training, it introduces Float8 data types and sparse training, ensuring resource efficiency. Its compatibility with PyTorch's `torch.compile()` and FSDP2 facilitates integration into existing workflows while supporting custom kernel development and experimental features. Suitable for researchers and developers looking to enhance performance while maintaining accuracy.