CTranslate2

Why we included this project

CTranslate2 skips the usual deep learning runtime and runs Transformer models through its own C++ engine with a Python wrapper. Layer fusion, batch reordering, padding removal, and weight quantization add up to noticeably higher throughput and lower memory use than a general-purpose framework on both CPU and GPU. You convert a model from tools like Hugging Face Transformers, Fairseq, or OpenNMT into an optimized format first, then load and generate through a small, predictable API. That extra step tends to pay off for teams serving translation, speech, or language models on constrained hardware. Quantization down to 4-bit AWQ and the documented backward compatibility guarantees also make it a reasonable choice for services you expect to run for years.