I'm not a vibe coder, but I would like some basic assistance with my code. I'm posting this because I feel like the general consensus on Reddit was misleading about which models would be best for me to run locally on a 16gb GPU for code assistance.
For context, I'm an early career academic with no research budget for a fancy GPU. I'm using my personal 16gb 4060ti to assist my coding. Right now I'm revisiting some numpy heavy code wrapped with @numba.jit that I wrote three years ago and it implements a novel type of reinforcement learning that hasn't been published. I've just spent several hours going through all of the recommended models. I told them explicitly that my code implements a type of reinforcement learning for a simple transitive inference task and asking the model to explain how my code in fact does this. I then have a further prompt asking the model to expand the code from a 5 element transitive inference task to a 7 element one. Devstral was the only model that was able to produce a partially correct response. It definitely wasn't a perfect response but it was at least something I could work with.
Other models I tried: GLM 4.7 flash 30b Qwen3 coder 30b a3b oss 20b Qwen3.5 27b and 9b Qwen2.5 coder 14b
Context length was between 20k and 48k depending on model size. 20k with devstral meant 10% was on CPU, but it still ran at a usable speed.
Conclusion: Other models might be better at vibe coding. But for a novel context that is significantly different that what was in the model's training set, Devstral small 2 is the only model that felt like it could intelligently parse my code.
If there are other models people think I should try please lmk. I hope that this saves someone some time, because the other models weren't even close in performance. GLM 4.7 I used a 4 bit what that had to run overnight and the output was still trash.
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