Asus releases new mini-PC internationally with Snapdragon X2 Elite and 32 GB RAM

[Redaktion]

Asus has released a new mini-PC internationally featuring the Qualcomm Snapdragon X2 Elite. Combining an 18-core processor with an 80 TOPS NPU, the Ascent QN10 also boasts 2.5 GbE LAN, three USB4 ports and up to 32 GB of LPDDR5X-8533 RAM.

https://www.notebookcheck.net/Asus-releases-new-mini-PC-internationally-with-Snapdragon-X2-Elite-and-32-GB-RAM.1396014.0.html

[Meh]

Meh. It's just a fancy Raspberry Pi.

[32GB total memory and AI]

and up to 32 GB of LPDDR5X-8533 RAM

If you plan using this laptop for AI (extensively):
Since the total memory (RAM + VRAM) in this laptop does not go above 32 GB: Know that with current SOTA AI LLM model Qwen3.6-35B-A3B[1] (in its size class) (only 3B parameters get activated per generated token -> perfect for RAM-only, no dGPU, devices), and its bang for the buck, 4-bit quant, you will be restricted to about this amount of context:

.. 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 this memory sense, any upgradable RAM + 6-8 GB VRAM gaming laptop (used for 700-800 bucks) is superior and much 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 huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF/blob/main/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf.

It is possible to stream the 3B active parameters from a SSD, but you'd have to inform yourself on how many tokens per second you'd be getting (ask/search e.g. on /r/localllama, /r/localllm).

More about running AI models locally: notebookchat.com/index.php?topic=315954.0 ("Your own ChatGPT, offline: AI without the cloud on your laptop") and comments.

[1] artificialanalysis.ai/?models=nemotron-3-5-lightning%2Cmuse-glimmer%2Cqwen3-8-27b-medium%2Cqwen3-6-35b-a3b%2Cqwen3-8-27b%2Cg9v3-39a5b%2Cgemma-4-31b&intelligence=artificial-analysis-intelligence-index#intelligence (Qwen3.8-27B (dense architecture -> 27B active parameters per token) scores much higher, but it's much slower) (you can look up non-reasoning scores or other models in the table)

[32GB total memory and AI]

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/