Eine wichtige Info wäre die Blende des Objektivs

Exactly.

About running AI LLM models locally and privately:
32 GB RAM - 8 GB for the OS itself = 24 GB free for fitting an AI LLM model.

The current SOTA AI LLM models, for their size, are: huggingface.co/Qwen/Qwen3.6-27B (dense architecture) (Downloads last month: 5.0 million) and huggingface.co/Qwen/Qwen3.6-35B-A3B (MoE architecture) (Downloads last month: 5.6 million).

A popular quant is huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF UD-Q4_K_XL (22.9 GB) (good for 80-120k unquantized. Context requires additional 2 GB for 32k and 8 GB for 128k context (linear)[1]. This means that the quant barely fits and there's pretty much almost or no space left for any context, see e.g. this quote:

Quote from: reddit.com/r/LocalLLaMA/comments/1sq94qx/is_anyone_getting_real_coding_work_done_with.. I've come to the conclusion that (1) 32768 is the biggest context I can get away with in an adequately smart model, and (2) it just ain't enough.

In a dense 27B model, all 27B parameters per token are used vs the 35B-A3B, where only 3B active are used. For a given quality, it's a speed (MoE model) vs size (dense) trade-off.

A huggingface.co/unsloth/Qwen3.6-27B-MTP-GGUF UD-Q4_K_XL quant (17.9 GB) would run slower, but at least it does somewhat fit with some context.

What would be needed are additional 8 GB VRAM dGPU (then it would be basically a gaming laptop) or a total of 48 GB system RAM.

Unfortunately this laptop does not support more than 32 GB RAM: psref.lenovo.com/WDProduct/IdeaPad/IdeaPad_5_2_in_1_14IPH11

[1] reddit.com/r/LocalLLaMA/comments/1tvluaj/how_much_vram_needed_for_qwen_36_27b_q8_with_262k

"Preis 2700 USD / Amazon 6.349,02 €"

Eure leidenschaftliche Werbung für Amazon;) Beachte das Verhältnis zwischen US- und Amazon-Preis...!