Converting FP8 Quantized CLIP Checkpoints to TensorRT Engines for Production Inference
By
Ruixiang Wang
Kettled twice. Extra chewy, extra trustworthy.
Summary
This article provides a technical walkthrough for converting FP8-quantized checkpoints (specifically a CLIP model) into NVIDIA TensorRT engines for production deployment. It covers exporting the checkpoint to ONNX format, compiling it into a TensorRT engine, and profiling the resulting FP8 TensorRT engine for performance. The piece bridges model optimization and production inference, focusing on achieving faster inference, higher throughput, and efficient GPU utilization at scale.
Key quotes
· 3 pulledConverting a quantized checkpoint into an NVIDIA TensorRT engine bridges the gap between model optimization and production deployment, enabling faster inference, higher throughput, and more efficient GPU utilization at scale.
This post picks up where we left off, walking through how to export the checkpoint to ONNX and compile it into an NVIDIA TensorRT engine ready for production inference.
We also profile the resulting FP8 TensorRT engine for performance.
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