FP4 quantization represents a significant advancement in AI model optimization by utilizing 4-bit floating point precision, which reduces model memory footprints and computational overhead while maintaining a dynamic range capable of encoding values between ±6.0. This low-bit quantization accelerates data processing, increases throughput, enhances energy efficiency, and allows scalable deployment of complex models on hardware with limited resources. The transition to FP4 precision involves techniques such as Post-Training Quantization and Quantization-Aware Training, with tools like NVIDIA TensorRT facilitating the process while ensuring high accuracy post-quantization. The benefits of FP4 are exemplified in models like FLUX, demonstrating up to a 3x increase in throughput and a 60% reduction in VRAM usage compared to FP16, all while maintaining image quality. NVIDIA’s Blackwell GPUs, optimized for FP4, offer significant performance improvements over H100 GPUs, making them ideal for scenarios requiring high efficiency and cost-effective deployment. Lambda’s 1-Click Clusters, powered by NVIDIA HGX B200, are engineered for native FP4 precision support, providing high performance, scalability, and ease of use for teams aiming to leverage FP4-optimized models, thus paving the way for broader adoption of advanced AI technologies.