Quote from: Olaf on September 12, 2026, 21:47:25Die Flip Nachtuhrfunktion gibt es bei Motorola s Flip schon ewig, bin daher nichts neues. Und eine durchscheinende Animation beim Klappen.... Ein sittlicher Mehrwert erschließt sich mir nicht wirklich. Sieht sicher toll aus. Aber was bringt's.

Finde die ich auch schön wenn ne gut tolle Animation da ist ,dadurch kann es aberNix weniger oder mehrAnimation. Finde es eher wichtiger das die Hardware mir passt zb keine einseitigen runden Ecken am Display finde ich eher negativ.

The Radeon 8060S iGPU is roughly as fast as a RTX 4060 Laptop (see 3dmark.com/search). The 128 GB RAM config is really useful for running MoE LLMs tho.

According to

reddit.com/r/LocalLLaMA/comments/1we8tl1/3827b_has_ruined_353635bs_for_me_its_just

, Qwen3.8-27B (dense) is, unsurprisingly and as can be seen in the previously mentioned artificialanalysis evaluation, a superior model VS Qwen3.6-35B-A3B for most tasks. So, if you are (really) ok with waiting many times longer for a much better answer (for simpler tasks, Qwen3.6-35B-A3B can be good enough and is much faster), you should consider huggingface.co/unsloth/Qwen3.8-27B-GGUF (Q4_K_XL). Due to also being smaller (27B vs 35B), Qwen3 27B has the advantage of giving you many more context tokens[1] on a 32 GB RAM-only device.

The slow speed of running a 27B dense quant on a DDR5-only device is ultimately going to annoy you, so, as previously mentioned, you should really think about getting a device with an at least 8 GB VRAM GPU, so that you can offload parts of the LLM into the much faster VRAM and run a ~27B dense, 4-bit quant, (much) faster VS a DDR5-only device.

[1]
Context: 16,384[KV cache context tokens per GB][2]*(32[GB]-6[GB for OS]-[quant filesize]):

• ~59,000 context tokens when using Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf = 16,384*(32-6-22.4)
  (or ~26,000 context tokens when giving the OS 8 GB, instead of 6 GB)
• ~138,000 context tokens when using Qwen3.8-27B-UD-Q4_K_XL.gguf = 16,384*(32-6-17.6)
  (or ~105,000 context tokens when giving the OS 8 GB, instead of 6 GB)

In other words, Qwen 27B more than doubles your available context tokens to approx. 76,800 (16,384*(22.4-17.6)).

[2] reddit.com/r/Qwen_AI/comments/1vo8pjz/qwen3827b_kv_cache_works_out_to_64_kibtoken_so/