Cuda Accelerated Tensor Operations For Efficiency

via “gpu acceleration with cuda support and memory optimization”

Fast transformer inference engine — INT8 quantization, C++ core, Whisper/Llama support.

Unique: Custom CUDA kernels for fused operations (attention, layer normalization, GEMM) with automatic GPU memory management and in-place operations, combined with dynamic memory allocation based on batch size. Unlike PyTorch CUDA kernels, CTranslate2 kernels are optimized specifically for inference workloads with minimal memory overhead.

vs others: 5-10x faster GPU inference than PyTorch due to fused kernels and memory optimization, while maintaining comparable accuracy.

via “cuda-accelerated tensor operations for efficiency”

Efficient and Effective Passage Search via Contextualized Late Interaction over BERT

Unique: Implements fused CUDA kernels that combine multiple operations (MaxSim, compression, aggregation) into single kernel launches, eliminating intermediate tensor materialization and reducing memory bandwidth by 5-10x compared to separate PyTorch operations

vs others: Faster than pure PyTorch implementations due to kernel fusion and reduced memory bandwidth, comparable to hand-optimized C++ implementations but with better maintainability through CUDA abstractions