Gebühr von 100€ bis 1.000€ je nach angeblicher Schwere der Sicherheitslücke. Schon wären die ganzen Betrüger aussortiert.

Ja, auch du(!), der meint mit ChatGPT "helfen" zu wollen, ist ein Betrüger.

Aber über dieses Tabu zu sprechen traut sich keiner.

About AI

Quote from: apple.com/newsroom/2026/08/apple-introduces-m6-and-m5-ultra-for-a-big-leap-in-performance-and-ai-computeM6 supports up to 32GB of unified memory to multitask across demanding apps and run LLMs on device for secure and private agentic tasks.

en.wikipedia.org/wiki/Apple_M6

Well, APPLE, problem it 32 GB RAM/unified memory only gives about this many context tokens for this SoTA AI LLM 35B MoE model (MoE -> fast on RAM/unified memory-only devices)[1]:

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.

(and this is on a 32 GB Mac, so it applies here perfectly)

Calculating available context: 16,384 [KV cache context tokens per GB]*(32 [GB total memory] - 6 to 8 [GB for the OS] - [quant filesize])[2]:
For huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF -> UD-Q4_K_XL.gguf:
~34,400 context tokens = 16,384*(32-7-22.9) (confirms the quote)

34,400 tokens = 25,800 words.
The 3-bit quant may not be usable at all, even at very low context (at 3-bit this model starts to degrade exponentially), but someone said, it works for their tasks to about 60,000 context tokens. The 4-bit Q4_K_XL quant is good for up to about 80,000 to 120,000 tokens, then the performance is going to degrade, depending on your task. So the higher the quenst, the better will it perform at more context.The full, 16-bit, weights model will be best for its native 262,144 tokens context (extensible up to 1,010,000 tokens).

I have the M5 Air and was hoping for a more than 32 GB RAM config, like at least 38-40 GB, preferably a bit more, ideally 48 GB.
In this memory sense, any gaming laptop that has a 6-8 GB VRAM GPU (used for 700-800 bucks) (it's also going to have upgradable RAM) is superior and cheaper, even if new. The additional 8 GB of memory/VRAM of the GPU make all the difference in being able to fit and run that SoTA model at a useful context size.

[1] artificialanalysis.ai/?models=muse-glimmer%2Cqwen3-6-35b-a3b%2Cqwen3-8-27b%2Cg9v3-39a5b%2Cgemma-4-31b%2Cgemma-4-26b-a4b%2Cqwen3-8-27b-non-reasoning&intelligence=artificial-analysis-intelligence-index#artificial-analysis-intelligence-index
(Qwen3.8-27B scores much higher, but due to being a dense model, will run many times slower)
[2] reddit.com/r/Qwen_AI/comments/1vo8pjz/qwen3827b_kv_cache_works_out_to_64_kibtoken_so/

I stopped trusting DXOMark a long time ago.

First, they were involved in a huge scandal when reviewing the Vivo X100 Pro, which was far superior to concurrent iPhones at the time. Despite a cursory look revealing that Vivo's photos were much more detailed, they said iPhones beat it.

Secondly, this prompted them to disable Disqus comments on their website. Without user feedback, their results become meaningless. They seemingly only care about selling their trademark, not the quality or breadth of their testing.

Thirdly, their results are not reproducible. They don't shoot the same scenes or subjects in a controlled environment. You cannot compare multiple smartphones that way. Sadly, however, it's not just DXOMark; the entire review industry is to blame.

Danke für die wichtige Information!