Here are some suggestions to help improve your model's CPU inference performance:

1. Reduce the batch size to 1 or just a few examples at a time. Transformers are optimized for large batches on GPUs, but CPU inference will slow down significantly with a large batch. Processing examples one-by-one or in very small batches can help.

2. Ensure your CPU code is optimized. Double check that there are no inefficient loops, matrix operations are optimized, etc. Basic code optimizations can provide modest CPU speedups.

3. Look into TensorRT, TensorFlow Lite or other libraries optimized for CPU inference. They may give better performance than pure PyTorch on CPU. 

4. Retrain a smaller version of your model for CPU deployment. This may lose some accuracy but can gain back a lot of speed. Transformers have a lot of parameters, so a smaller model goes a long way!

5. Unfortunately, CPUs just can't match the raw speed and computational power of GPUs, especially for workloads meant for GPUs like transformers. You may have to accept some performance lag when deploying on CPU.

Those are a few suggestions to experiment with to try and improve your CPU inference. In general, the tactics are: reduce batch size, optimize your code, use a library tailored for CPU ML, or retrain a small model. Let me know if any of these options help speed up your model! I'm happy to provide any other advice or ideas.
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