RTX 5070 Ti fits PEFT, not training

This is a solid “learn by doing” GPU, but not the dream box many newcomers think they’re buying.

• –16GB of VRAM is enough to get into local inference and parameter-efficient fine-tuning on smaller models, which makes it a practical entry point for hands-on LLM work.
• –Full fine-tuning is a different class of workload: rule-of-thumb guidance puts a 7B model far above 16GB once optimizer state and gradients are included.
• –The 32GB system RAM choice is the weaker part of the build; once you add datasets, dev tools, containers, and background processes, 64GB becomes the safer baseline.
• –For buyers optimizing strictly for local AI value, older 24GB-class cards still punch above their weight when the workload is bigger models rather than gaming.
• –The real question is intent: for learning PEFT and local tooling, this setup is good enough; for ambitious training runs, it will feel constrained fast.