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Model Quantization: Turn FP8 Checkpoints into High-Performance Inference Engines with NVIDIA TensorRT
This article is the third in a series on model quantization, focusing on converting FP8 quantized checkpoints into high-performance inference engines using NVIDIA TensorRT, enabling faster inference and higher throughput.
This post is the third of a three-part series. See also Model Quantization: Concepts, Methods, and Why It Matters and Model Quantization: Post-Training Quantization Using NVIDIA Model Optimizer. Converting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughput, and efficient AI model deployment.