How low-bit inference enables efficient AI

This article explores low-bit inference techniques that make large AI models faster and more cost-efficient to serve in production.

• •Matrix multiplications in linear layers and attention mechanisms dominate compute in attention-based models, handled by NVIDIA Tensor Cores or AMD Matrix Cores
• •Quantization reduces numerical precision (e.g., 16-bit to 4-bit), cutting memory footprint and energy use; halving precision can roughly double throughput on modern hardware
• •Pre-MXFP formats use explicit dequantization before matrix multiply, with A16W4 relying on techniques like AWQ or HQQ to preserve quality at low bit widths
• •Weight-only quantization (A16W4) suits memory-bound, small-batch workloads; activation quantization (A8W8) suits compute-bound, high-throughput scenarios
• •MXFP microscaling formats move scaling operations directly into Tensor Core hardware, enabling native low-bit compute without software-managed dequantization overhead