Shubhra Pandit

Neural Magic

We are building software that makes machine learning execution simple and efficient. What does that mean? Together with our community, we engineer sparse NLP and CV models that are more efficient and performant in production. They recover to baseline accuracy and you can apply your data using only a few lines of code. Why does this matter? Sparse models are more flexible and can be deployed at extreme speeds without GPUs. Using DeepSparse, our sparsity-aware inference runtime, you can achieve unrivaled latency and throughput performance using commodity CPUs you already own. Check us out on GitHub and join the Neural Magic Slack community to help us accelerate our vision of software-delivered AI.