QwertyChouskie, good points, but an increase from 8 to 12 CUs is not a +50% improvement in practice (the performance increase doesn't scale linearly with the number of CUs).

Quotebrings probably a 2x or greater performance leap over the Deck 1

Using hardware-based RDNA4 upscaling with FSR4 on top of the nodes improvement is going to to certainly improve the perf by 2x (2 = 1.52 * 1.3 (what upscaling at good quality delivers).

Schauen wir mal ob die "for Galaxy" Variante den Wert noch toppen wird und weiter an Apple A20 Pro aufschließen kann.

QuoteEven the current crop of Intel-based handheld gaming consoles like the MSI Claw 8 EX AI+ with the Intel Arc G3 Extreme will likely be much more powerful than the Steam Deck 2.

True, Intel's Xe3 Panther Lake iGPU cores are more power efficient (but Intel is also using TSMC's next gen 3N node vs AMD using 5N) and so a Steam Deck 2 based on them would be faster. But it doesn't mean that AMD couldn't do it. FSR4 / upscaling would be especially helpful in handhelds.

QuoteSo, only time will tell what APU the Valve Steam Deck 2 actually employs and how powerful the handheld console ends up being.

When using only a full node shrink:
Power reduction at same speed: ~25–30%.
Speed gain at same power: ~10–15%.

AMD Ryzen Z1 APU is using TSMC's 4N node (slightly improved 5N -- basically 5N), and by 2028 TSMC 2N node will be available in the sense that it's also going to be cheap enough by then, then using 2N node, the raw performance would have improved by 2 nodes (5N -> 3N -> 2N).

Using a 2-node improvement: 1.15 * 1.15 (= 1.15^2) = +32% FPS per Watt improvement.

Using a 3-node improvement / including TSMC A14 node:
1.15^3= +52% FPS per Watt improvement.

Also Perfekt für mich.
Ich spiele seit Jahren nur noch Indie-Games, da ich die Zeit einfach nicht dazu habe, länger als 4 Stunden pro Woche zu Zocken.

Jetzt muss nur noch der Preis passen.

Quoteup to 32 GB of LPDDR5X RAM

This is unfortunately on the cusp of being usable, because 32 GB of total memory/(V)RAM you can fit about this many context tokens of SOTA (in its class size) AI LLM model[1] huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF 4-bit UD-Q4_K_XL quant:

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.

(1 token = 0.75 words)

It's also possible to calculate the available context[2]: 16,384 [KV cache context tokens per GB]*(32 [GB total memory] - 6 to 8 [GB for the OS] - [GB quant filesize]):
~34,400 context tokens = 16,384*(32-7-22.9) (confirms the quote)

On Windows and with the MTP variant (=faster token generation) (with MTP head: 22.9 GB (vs without MTP: 22.4 GB)), the context tokens are going to be even less than what is in the quote.

Agentic workflows often exceed 60,000 context tokens.

In this memory sense, any gaming laptop that has a 6-8 GB VRAM GPU is superior, possibly even cheaper and also going to have upgradable RAM.

Recalculating with an additional 8 GB VRAM GPU:
165,500 = 16,384*(32-7-22.9+8).

And, generally, you get more memory per buck if you get a desktop PC.

[1] artificialanalysis.ai/?models=qwen3-8-27b%2Cqwen3-8-27b-medium%2Cqwen3-8-27b-low%2Cqwen3-8-27b-non-reasoning%2Cqwen3-6-35b-a3b%2Cqwen3-6-35b-a3b-non-reasoning&intelligence=artificial-analysis-intelligence-index
[2] reddit.com/r/Qwen_AI/comments/1vo8pjz/qwen3827b_kv_cache_works_out_to_64_kibtoken_so/

The OSA regulation has already done damage to indie games and small companies.
This will completely obliterate the gaming market and will leave us only with the likes of EA and Ubisoft.