You're right, I misspoke - C/C++ are not technically native machine code. They are compiled languages that produce native machine code. Some tips to improve CPU performance for your PyTorch model:

1. Use a lower batch size. Large batches benefit more from GPU parallelism. On CPU, smaller batches will be more efficient.
2. Reduce model complexity. Simpler models with fewer layers/parameters will run faster on CPU.
3. Use half-precision floats (float16) instead of single precision (float32). This reduces memory usage and computation.
4. Use multithreading. PyTorch supports multithreaded data loading and processing. This can utilize multiple CPU cores.
5. Optimize your code. Some tips:
- Avoid excessive tensor reshaping and copies. Reuse tensors when possible.
- Use in-place operations like tensor.add_() instead of tensor = tensor + other_tensor.
- Avoid too many small tensor allocations. Reuse larger tensors.
- Use CUDA tensors even on CPU - they have the same memory layout so models trained on GPU will transfer to CPU more efficiently.
- Make sure you're using the latest version of PyTorch which has the latest performance optimizations.
6. You can look into using Intel MKL or OpenBLAS for optimized CPU performance of linear algebra ops. PyTorch supports both of these.
7. ONNX export + runtime inference is an option. The ONNX runtimes are optimized for CPU inference. So you can export from PyTorch, then load in the ONNX runtime.
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