For what it's worth, I ported the sum example to pure python.

    def sum(depth, x):
        if depth == 0:
          return x
        else:
          fst = sum(depth-1, x*2+0) # adds the fst half
          snd = sum(depth-1, x*2+1) # adds the snd half
          return fst + snd
        
    print(sum(30, 0))

under pypy3 it executes in 0m4.478s, single threaded. Under python 3.12, it executed in 1m42.148s, again single threaded. I mention that because you include benchmark information:

    CPU, Apple M3 Max, 1 thread: 3.5 minutes
    CPU, Apple M3 Max, 16 threads: 10.26 seconds
    GPU, NVIDIA RTX 4090, 32k threads: 1.88 seconds

The bend single-threaded version has been running for 42 minutes on my laptop, is consuming 6GB of memory, and still hasn't finished (12th Gen Intel(R) Core(TM) i7-1270P, Ubuntu 24.04). That seems to be an incredibly slow interpreter. Has this been tested or developed on anything other than Macs / aarch64?

I appreciate this is early days, but it's hard to get excited about what seems to be incredibly slow performance from a really simple example you give. If the simple stuff is slow, what does that mean for the complicated stuff?

If I get a chance tonight, I'll re-run it with `-s` argument, see if I get anything helpful.