>fast LPDDR5X RAM (7,467 MT/s)

The integrated memory controller on the Core Ultra X7 358H officially supports dual-channel memory speeds up to LPDDR5X-9600 MT/s (as well as LPDDR5X-8533 MT/s).
Intel's OEM Branding Requirement: Intel established 7,467 MT/s as the strict baseline threshold for LPDDR5X RAM in laptop designs using Panther Lake processors

=> maybe it is fast but it could be much better ...

Quote32 GB of RAM

QuoteRAM not expandable

If you plan using this mini-PC for AI (extensively):
Since the total memory (RAM + VRAM) in this mini-PC 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:

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.

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)

If you still want this 32 GB total memory mini-PC: 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[2] 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 additional 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.

[2]
Context: 16,384[KV cache context tokens per GB]*(32 [GB total memory] - 6 [GB for OS] - [quant filesize])[3]:

• ~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)

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

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

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For 1500 bucks you can get a desktop PC with more of total memory and it will be faster, too, because it will have a good gaming GPU with 12 GB of VRAM:

• this: 3808 AI score = 32 GB RAM * 119 GB/s (=128*7467/1000/8)

vs

• desktop PC build with a RTX 4070: 8915 AI score = 32 GB RAM * 89.6 GB/s (=128*5600/1000/8) + 12 GB VRAM * 504 GB/s

You may also be able to get 2*24 GB of RAM.