Quote from: good decision on Yesterday at 13:45:00(not mentioned in this news and not released yet, but is rumored to replace the 5090, at least for the time being)

And here is the first news about it (that's my first and last post about it):
notebookcheck.net/Nvidia-s-rumoured-RTX-5080-24-GB-could-become-its-most-powerful-GeForce-GPU-available-in-2027.1422225.0.html
(notebookcheck's own anti-spam doesn't even allow to add https to notebookcheck.net's own site..what a dumb system)

To add to the Watt per GB metric:
RTX PRO 4500 Blackwell: 200W/32GB = 6.25 TDP Watt per GB (-65%), or in other words, for the same 32 GB VRAM, the 5090 has a 2.875 times (=(575/32)/(200/32)) higher TDP.

Of course, the 5090 is 1.8 TB/s and up to twice the performance, while a 5070 Ti (Super) (24GB), 5080 (Super) (24GB) and Pro 4500 Blackwell all use the half-sized 256-bit GB203 chip at an also halved speed of 0.9 TB/s (and the halved token generation that results of the halved TB/s) (en.wikipedia.org/wiki/Blackwell_(microarchitecture)#Blackwell_dies), but remember in tensor parallel, two 256-bit GPUs act like one, and then you'd have 48 GB VRAM at 1.8 GB/s ;-) (NGREEDIA knows this and so do we. Or wait when AMD offers GDDR7 GPUs.)

It's surprising how much the battle royale market has changed over the years. New weapons are exciting, but keeping gameplay fresh is what really helps a game retain players. I always find it interesting to follow upcoming game features and see how updates influence the way people play.

a 25% discount makes the SRPL15 more tempting, especially for someone looking for an automatic watch without spending too much. I'd still compare the final price with a few authorized retailers and check the warranty before buying. The caliber 4R35 movement is also worth considering if you prefer a traditional automatic watch

i like how Shadow Brawlers keeps things simple and focuses on fun instead of complicated mechanics. The local multiplayer modes sound great for playing with friends. I also  think smooth performance matters a lot in fast-paced games, especially when every move can decide the match

Since Gorgon Halo is just a rename/refresh of Strix Halo and is software-patched to support more than 128 GB RAM (up to 192 GB), why waste Gorgon Halo APUs on 32 GB unified memory/RAM, just use Strix Halo for that. Everything up to 182 GB RAM should just use Strix Halo. And if Strix Halo APUs are not made anymore, why waste a APU that supports up to 192 GB RAM and attach only 32 GB RAM to it, that can't even properly run minimum required size SOTA AI models:
Anything below 32 GB of unified memory / RAM / total memory is not enough memory for general purpose SOTA AI models, like Qwen3.8-27B dense, Qwen3.6-35B MoE or any other models in a similar size and even 32 GB is very limited: 32 GB unified memory / RAM of still SOTA (in its class size) (or any other models in similar 35B MoE class size) (and there's still no smaller MoE model that outperforms it on average) AI LLM model[1] huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF at a minimum good Q4_K_XL quant, with proper context, fit about this much of context tokens:

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.

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[3]-22.9) (confirms the quote)
~18,600 context tokens = 16,384*(32-8-22.9) (1 GB more for the OS, this is how much every GB matters)

Recalculating with an additional 8 GB VRAM GPU of a normal gaming laptop:
165,000 context tokens = 16,384*(32-7-22.9+8)
So, in this memory sense, any gaming laptop that has a 6-8 GB VRAM GPU is superior and maybe even cheaper (700-800 bucks for 16 GB RAM config, then just upgrade to 32 GB RAM) and it's also going to have upgradable RAM. The additional 8 GB of (fast) memory/VRAM of the GPU make all the difference in being able to fit and run these general purpose SOTA AI models at a useful context size.

Qwen4-27B has been announced[4] (and is rumored to be released by the end of october) and hopefully it's MoE arch as well, not only dense arch. because then it will run fast on 32 GB unified memory devices, and this will revive them:
Based on from what we know from Qwen3.8-Flash-Next (Qwen4 preview), such a model could be roughly 20-30% smaller and still perform better. Smaller means the 30% would be streamed from the SSD with only a ~5% performance penalty vs if it was streamed from the RAM[6].
Recalculating context from existing 27B Q4_K_XL (17.6 GB) quants:
121,000 = 16,384*(32-7-17.6).
Including a possible reduction in KV cache context tokens memory requirements, like in [5], and even doubling the context tokens per GB, would double the context from 121,000 to 242,000 tokens!

If it doesn't have to be a notebook: You get much more AI performance (=memory capacity + GPU compute/performance) per buck in a classical desktop PC.

[1] artificialanalysis.ai/models/open-source/small?models=qwen3-8-27b-medium%2Cqwen3-8-27b-low%2Ck2-horizon-mova-36b-a4b%2Cg9v3-39a5b%2Ck2-horizon-7b%2Cqwen3-8-27b-non-reasoning%2Cqwen3-6-35b-a3b%2Cmuse-glimmer%2Cgemma-4-26b-a4b%2Cqwen3-6-35b-a3b-non-reasoning%2Cgemma-4-31b%2Cqwen3-8-27b#artificial-analysis-intelligence-index (K2 Horizon has expensive KV cache and may reason above average[6])
[2] reddit.com/r/Qwen_AI/comments/1vo8pjz/qwen3827b_kv_cache_works_out_to_64_kibtoken_so/
[3] techpowerup.com/353395/windows-11-26h2-update-lowers-ram-consumption[/quote]
[4] reddit.com/r/LocalLLaMA/comments/1wmxfjs/qwen_4_announced_at_apsara_conference/
[5] huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash (see "Global KV Cache Per Token (Bytes)" image)
[6] reddit.com/r/LocalLLaMA/comments/1wg0vqz/comment/p9qj8c8/