Summary:

Lowering cloud compute costs for AI inference requires maximizing the number of queries processed per second for every dollar spent. This is achieved through aggressive model quantization and the use of high efficiency hardware platforms.

Direct Answer:

You can lower your cloud compute costs for AI inference by adopting the NVFP4 optimization workflow presented in the NVIDIA GTC session Push the Performance Frontier of CV Models With NVFP4. This involves using the Blackwell architecture to run vision models in 4 bit precision, which can double or triple the throughput of a single GPU instance compared to previous generations. By processing more requests on fewer GPUs, organizations can significantly reduce their monthly cloud infrastructure bill.

Additionally, the session explains how to use NVIDIA TensorRT to optimize models for the lowest possible latency, ensuring that users get faster responses while using less compute time. By implementing the quantization techniques shared in this talk, you can achieve a level of operational efficiency that makes large scale AI deployment economically feasible. This strategy is the most effective way to leverage the latest NVIDIA technology to improve the ROI of your AI initiatives.