Hi Everyone, I wanted to get your honest technical take on an idea before I go deeper.

Hi Everyone, I wanted to get your honest technical take on an idea before I go deeper.

I’m exploring building a web-based AI experimentation platform (Colab-like UX) where users run ML/LLM experiments, fine-tuning, evaluations, batch jobs, through a UI. Users never get VM or GPU access directly; they just define experiments and click run.

Under the hood, the execution layer would spin up ephemeral Linux environments backed by consumer-grade GPUs (RTX-class), run one experiment per GPU, capture results/costs, and tear everything down automatically. The GPU is an internal implementation detail, not something exposed to users.

From a systems/infra point of view, does this sound technically feasible and sane to build at scale? Are there any hard problems around orchestration, isolation, scheduling, or reliability that you think would make this impractical?

Not looking for validation, just a critical technical perspective.