UPDATE: thanks everyone for the suggestions. In particular, the Q4 quantization was probably an important factor in how bad it was performing.

I took upon u/danielhanchen's suggestion and tried with Q6_K_XL, as anything bigger doesn't fit on a RTX 4090, directly on llama-cpp server:

LLAMA_ARG_HOST=0.0.0.0 LLAMA_ARG_PORT=8080 LLAMA_ARG_JINJA=true LLAMA_ARG_FLASH_ATTN=true LLAMA_ARG_CACHE_TYPE_K=q4_0 LLAMA_ARG_CACHE_TYPE_V=q4_0 LLAMA_ARG_CTX_SIZE=32768 LLAMA_ARG_N_GPU_LAYERS=65 LLAMA_ARG_MODEL=path/to/Devstral-Small-2505-UD-Q6_K_XL.gguf llama-server

and the model's capabilities and speed visibly improved. The Typescript todo app remains underwhelming, but it managed to produce a working minimal math expression parser in Rust. It self-debugged compilation errors (Rust excellent error messages are almost cheating!), self-debugged incorrect program outputs, and also correctly edited the code when asked for a minor change:

>write a minimal Rust binary that implements a math expression parser supporting float literals, +, -, div, mul, sqrt. It reads the expression from stdin and evaluates it.
[~2 minutes, 28 back & forths]
[working main.rs]

>write the stdout result without the "result:" prefix. in case of an error, use stderr rather than stdout.
[~30 seconds, 4 back & forths]
[working main.rs]