Default Hardware Assumptions
The original AutoResearch repository is built around a one-GPU workflow and the README notes testing on H100-class hardware. That makes the default experience very different from what a laptop or small consumer GPU can comfortably handle.
What Usually Needs to Change?
- use a smaller dataset with lower entropy
- reduce vocabulary size
- lower sequence length
- reduce batch size and model depth
- avoid settings that are inefficient on your platform
When to Use a Fork
If you are on Mac, Windows, or AMD, a fork may save you significant setup time. The original repository already points readers toward notable forks for those environments, which makes them the best place to start if you are outside the main NVIDIA path.
FAQ
Can AutoResearch run on a MacBook?
Possibly through forks and lower-compute adaptations, but not with the default expectations of the original setup.
Do I need an H100?
No, but the original repo was tested on that class of hardware, so smaller systems will usually need adjustments.
Should I use the main repo or a fork?
If your hardware differs substantially from the default NVIDIA setup, a fork is often the faster path.