&gt; But as Numba has shown, it&#x27;s achievable to write a CPython-to-LLVM-IR compiler for subsets of the language, then add NumPy-awareness, and add multiple targets like GPUs, and achieve better-than-PyPy speed ups jitting pure Python (CPython) code.<p>Other than &quot;to-LLVM&quot; and &quot;targets like GPUs&quot;, jitpy does exactly that for PyPy-in-CPython.<p><a href="http:&#x2F;&#x2F;jitpy.readthedocs.org&#x2F;en&#x2F;latest&#x2F;" rel="nofollow">http:&#x2F;&#x2F;jitpy.readthedocs.org&#x2F;en&#x2F;latest&#x2F;</a><p>It&#x27;s not quite as fast for numeric code (though it&#x27;s close <i>and</i> conformant) but it&#x27;s also not a language subset nor does it degrade to CPython performance on dynamic parts of the language. It should be a terrific option if you&#x27;re able to section out discrete workloads that CPython&#x27;s struggling with, even in non-numeric code.<p>Few people know of it though.