... für ein Tunnelgebilde mit unterschiedlicher Fallhöhe ... sicherlich interessant.
Ich denke an die Rocky Moneytans in den USA, im Katastrophenfall, da wären Tunnels gut geeignet.

Since it's the same device, I'll just quote:

Quote from: Linux? on Today at 08:51:39Can Linux be installed? I read the bootloader is locked?

Quote from: ye idk on Today at 10:10:03

QuoteHere, the GPU performance is comparable to powerful versions of the Nvidia GeForce RTX 5070

Clicking on "Steel Nomad Score", Spark only 12% faster than the 5060 Laptop (Legion 7 16 AGP11) and 4-9% faster than the Radeon 8060S in "Cyberpunk 2077 / Ultra Preset (FSR off)", which checks out considering the ~300 GB/s (could be lower in this particular device, depending on the LPDDR5X MT/s used) (vs Strix Halo's 256 GB/s) memory speed.

Spark N1X: 301 GB/s = 256-bit * 9400 MT/s (assumed) / 1000 / 8.

QuoteLocal AI powerhouse. [..] The new chips are designed for AI workloads, [..]

PS: For AI, AMD's Gorgon Halo, a Strix Halo rename/refresh with software-patched support from up to 128 to up to 192 GB unified memory/RAM, at a similar bandwidth, is an alternative (NVIDIA's CUDA is not required for end-user inferencing tasks (maybe there are special cases, I don't know)); if you want to fine-tune or even train AI models (which would may be slow on these ~300 GB/s )), CUDA is the standard, but it doesn't mean that it can't be done on AMD hardware) for those who require more than 128 GB unified memory/RAM.
Gorgon Halo: 273 GB/s = 256-bit * 8533 MT/s / 1000 / 8.

I wish these (256-bit) APUs had double the unified memory (to 256 GB) and double to quadrupled the memory speed, to be really interesting. Of course, price is another issue too, but first, the specs need to double (and no, I'm not buying 2 of these).

AI bang for buck performance alternative
For more AI bang for buck performance and upgradable parts, get a consumer desktop PC 96 to 128 GB RAM (a consumer desktop PC uses 128-bit memory interface (aka dual-channel) and the RAM's MT/s is typically between 5600-6200 MT/s, which equals to ~96 GB/s (vs Spark's ~300 GB/s), but the GPU(s) will make up for it (see calculation below) (32 GB VRAM at ~1800 GB/s, when running 2 5070 Ti (each at ~900 GB/s) in tensor parallel) + 1 GPU (at lest like a 5070 Ti (16 GB VRAM at 900 GB/s, it will have much faster prompt processing and you will have 128+16 GB of total memory) (a 5080 24 GB VRAM is rumored to be released in 2027 Q1)) or may get a motherboard that supports bifurcation (geizhals.eu/?cat=mainboards&xf=2833_AMD+AM5~2873_ab+2&pagesize=30&promode=true) and get 2 GPUs of the same GPU architecture and run them in tensor parallel mode, which will double the VRAM memory in size and speed (reddit.com/r/LocalLLaMa/search/?q=tensor parallel).

Let's calculate our arbitrary AI score (memory size times memory speed):
N1X Spark: 38400 = 128 GB unified memory * 300 GB/s.
Desktop PC build using two 5070 Ti and running them in tensor parallel: 69888 = 128 GB * 96 GB/s + 32 GB VRAM * 1800 GB/s.

Quote from: ye idk on Today at 10:25:45The AI models that are best suited for these ~300 GB/s APUs (Strix/Gorgon Halo, Spark N1X, APPLE's M Pro) are of MoE (as opposed to dense) architecture, see e.g.:

artificialanalysis.ai/?models=qwen3-5-122b-a10b-non-reasoning%2Cglm-5-3-flash%2Cqwen3-5-122b-a10b%2Cmimo-v2-5-0424%2Cnemotron-3-5-lightning%2Cqwen3-6-27b-non-reasoning%2Cminimax-m2-7%2Cmuse-glimmer%2Cnvidia-nemotron-3-super-120b-a12b%2Cqwen3-8-27b-medium%2Cqwen3-6-35b-a3b-non-reasoning%2Cstep-3-7-flash%2Cinkling-small%2Csolar-open2-250b%2Cqwen3-6-35b-a3b%2Cqwen3-6-27b%2Cqwen3-8-flash-next%2Cqwen3-8-27b%2Cmotif-3%2Chy3%2Cg9v3-39a5b%2Cgemma-4-31b%2Cgemma-4-31b-non-reasoning%2Cmistral-medium-3-5%2Cling-3-0-flash%2Cgpt-oss-120b%2Cdeepseek-v4-flash%2Cnemotron-3-5-bolt-74b-a9b%2Cnemotron-3-5-super-vl&intelligence=artificial-analysis-intelligence-index

Qwen3.8-Flash-Next is basically the winner overall (highest performance per size) and has only 6B active parameters, perfect for ~300 GB/s/Spark N1X devices.

NVIDIA also offers specific NVFP4 builds of Qwen3.8-Flash-Next for Spark specifically, since it also supports NVFP4:
huggingface.co/nvidia/Qwen3.8-Flash-Next-NVFP4 (133 GB but the n-gram may be streamed from the SSD (-> 133GB - 30% (streamed from a SSD) = 93 GB -> fits in a 128 GB unified memory Spark config)
huggingface.co/nvidia/Qwen3.8-Flash-Next-FP4-Local (this one is new and should definitely fit in a 128 GB unified memory config)

Quote6699 Euro

If you're still interested, you might as well:

Quote from: youtube.com/watch?v=Z26kN5VfyGAAMD and the industry are pushing the Ryzen AI Max+ 495 very hard as serious AI desktops, but the marketing is getting ahead of the actual performance. I own both a 128GB Framework Desktop and a 128GB HP Zbook laptop on this platform, so I know what they can do. Yes, the huge unified memory lets you load  large models, but in many cases you're running heavily quantized versions, which inevitably sacrifice model quality. And with only ~273 GB/s bandwidth versus around 1.2 TB/s on an M5 Ultra at about €10,500 for 256GB ram, inference is simply too slow for what they are now charging. Last year, the 128GB Framework Desktop with the Ryzen AI Max+ 395 was around €2,500 now we're talking about €8,500 for 192GB. If you're a developer, you'd be far better off paying €80–€200/month for a serious AI coding subscription and getting much stronger models. And if you're a company that absolutely needs local AI for data privacy, then spend more and buy genuinely high-performance hardware. These are interesting machines, but at €8,500 they are being massively oversold. 👎