How can I optimize Hugging Face Transformers for low-latency production inference?

All answers to this question.

You should definitely explore the Hugging Face Optimum library. It provides a simple interface to export your models to ONNX or OpenVINO. For Intel CPUs, OpenVINO can often cut latency by 5x through graph optimizations and quantization. Another quick win is to use dynamic padding during inference so you aren't processing unnecessary tokens. I reduced a project's latency from 250ms to 45ms by converting our model to an ONNX graph and using the ORTModelForSequenceClassification class. It requires very minimal code changes but yields massive performance gains in production environments.

   Answered 2025-11-11 by Karen Mitchell

Does converting to ONNX affect the model's accuracy, particularly if I apply post-training quantization to the weights?

   Answered 2025-11-12 by Joseph Hall

• Joseph, there is usually a very slight drop (less than 1%), but for most business cases, the speed boost is worth it. If accuracy is critical, you can perform Quantization-Aware Training (QAT), which helps the model adapt to the lower precision during the fine-tuning stage.

     Commented 2025-11-13 by Susan Lopez

Also, don't forget to check if your model can be distilled. A DistilBERT model is much faster and often nearly as accurate.

   Answered 2025-11-14 by Richard Moore

• Great point, Richard. Sometimes a smaller architecture is the best optimization of all.

     Commented 2025-11-15 by Karen Mitchell