Years ago I wrote a small toy interpreter based on predereferenced computed gotos with surprisingly good speed. For very small programs without dynamic data allocation churn it could run as fast as 3 times slower than compiled code instead of the expected 10X slower. Good things happen speed-wise when all the byte code and data fits into the L1 cache. Also, branch prediction is near perfect in tight loops.

Sounds like a great test case for using a tensorflow-lite model to switch between both techniques.