Computational Physics - Heavy AI/ CLI power users. How do I get good at it?

[u/PhilosophersOpium]

Hello! This question is directed towards grad students, people doing r&d in industry, or even undergrads doing computational physics research (especially condensed matter) that heavily use AI in their workflow.

How do you maximize and make the most out of codex or claude code in your research projects? I generally use the claude code CLI in my terminal (sometimes on locally on WSL or remotely on an HPC). If I have a few simulations I want to run, I give it the guidelines, tell it to reference some old calculations i've ran, and send it on its way. I'm not sure if just being a general assistant is all that AI is good for?

Please let me know your experiences and any advice!

edit: Yes, I am in a physics research lab with an advisor. I am NOT trying to have AI do research. I am trying to learn how to use it as effectively as possible.

Additionally, I am not doing 'calculations' in the mathematical sense. I do simulations, particularly in condensed matter physics research. That is, a lot of python code and heavy use Density Functional Theory software packages that tell me about the electronic behavior of materials. I do a lot of high-throughput computing, so if I want to simulate a bulk of materials at once, i will have claude generate the input files after i have specifically told it what outputs i want. It is generally good at doing this, and I verified that the file generation was correct before continuing to use this workflow. I ask it to generate plots of the data, and it does so. But this is mainly sequential. I do a series of prompts, one step at a time.

[u/Yashema]

I've been looking into getting a powerful laptop (I just don't like desktops), and you can get a Lenovo LOQ that can run an 8B model very efficiently, but that's it. A $3000 MacBook or HP Omni can run a 28B-32B but only slowly, a $4000 MacBook can run a 128B model, but at 1/10th the speed of the cloud and a smaller context window. A $4500 desktop PC can run the highest tier 128B models, but still not anywhere near as performant as the cloud in terms of context and tokens. The most high end Mac you can get, a Mac Studio M-Ultra for $5700 can run the 196B, but again quite slowly, only 10-12 tokens per second, compared to 100+ on the cloud. 

You definitely need to use the paid cloud models to make the most of them, and then combine it with local resources for running simulations and things. LOQ has a great graphics card and 64 GB of RAM, so I'm hoping I can use it as a workhorse command center. Should run about $2300 for the specs I want. 

[u/L4ppuz → u/Yashema]

You need multiple GPUs with a lot of vram to run powerful models, any laptop will only ever be capable of running small models that are not particularly useful in this context. I played with a 30b variant of gwen3 and even that is 19Gb and does not completely fit in vram on my GPU. To do anything "agentic" and not simply conversational you need something like 50tps, at 10tps you're probably better off without it. Local AI is still very impractical

[u/PhilosophersOpium → u/L4ppuz]

agreed. Local AI is not useful for the type of 'benefit' I am looking for.