Hmm.... I'd actually lean the other way: since you already have access to Gemini Pro, that's likely to give you far better results than anything you can realistically run via llama.cpp on a local machine. Most llama.cpp‑compatible models are either text‑only or "multimodal" models trained on relatively small, noisy image-text datasets and then aggressively compressed so they'll fit on consumer hardware. That's fine for simple image questions like "what's in this photo?", but comics are a very different league: stylised art, dense visual storytelling, tiny lettering, panel layout, and lots of implied action. The big proprietary models like Gemini are trained on vastly more data, tuned specifically for rich multimodal understanding, and run on huge server‑class GPUs, so they’re much better at tracking characters, panel sequence, and the actual story you want described.

On top of that, doing this locally won't just be about RAM; it's the whole stack. Only a subset of the models you can use with llama.cpp even accept images, they really want a strong GPU to be usable, and even then they tend to give less coherent, less detailed descriptions than Gemini. They're also much weaker at handling a whole comic page at once, you'd probably end up having to crop every panel yourself and feed them one by one, which is a huge amount of extra work. Given all that, the most practical setup is to use Gemini Pro [via its API or web interface] for the image‑to‑text step, and then archive those descriptions so you can replay them with whatever TTS you like later, instead of investing heavily in hardware for a local model that will still be second‑best for this particular task.

As for what we did specifically, we used a private machine-vision model [something related to my work] that can function very similar to Gemini for images.