How low-bit inference enables efficient AI

This post details how Dropbox leverages low-bit inference techniques, specifically quantization, to improve the efficiency and reduce the cost of running large AI models for products like Dropbox Dash. It explains the trade-offs between different quantization formats (e.g., 16-bit to 4-bit), the role of hardware acceleration (Tensor Cores, Matrix Cores), and the challenges of bitpacking for sub-byte formats. The post highlights how these optimizations are crucial for delivering responsive user experiences and managing resource usage in production AI workloads.