llama.cpp auto-fit unlocks bigger local models

This is not a formal benchmark, but it is a useful signal: llama.cpp’s auto-fit path is becoming practical enough that local AI builders should revisit old VRAM assumptions.

• –`--fit` automatically adjusts placement instead of forcing users to hand-tune GPU layers, tensor splits, and memory headroom.
• –The result matters most for oversized local models, long-context coding setups, and users trying to stretch consumer GPUs.
• –Commenters note KV cache quantization and `--fit-target` can further change the speed/context tradeoff, so configuration still matters.
• –The caveat is that this is anecdotal Reddit data, not a controlled benchmark across models, backends, and prompts.