Wow, those DPC latencies will sure make using this laptop enjoyable... Unbelievable incompetence, just straight up embarrassing in this day and age.

2500 bucks for only 32 GB soldered RAM and 8 GB VRAM.

If gaming is the primary target -- 8 GB VRAM:
youtube.com/watch?v=ric7yb1VaoA: "Gaming Laptops are in Trouble - VRAM Testing w/ ‪@Hardwareunboxed‬".
12 GB VRAM on the same 128-bit bus are around the corner (using 3 GB instead the current 2 GB dense GDDR7 chips) according to: notebookcheck.net/Lenovo-confirms-RTX-5070-12GB-gaming-laptops-launching-soon-with-Intel-Core-Ultra-7-251HX-models-also-joining.1255561.0.html

If running AI / LLMs locally is the primary target:
If this hadn't the 8 GB VRAM GPU, then the 32 GB RAM would not be enough for the new SOTA LLM, Qwen3.6-35B-A3B-UD-Q4_K_M, in agentic workflows:

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.

But since this laptop has additional 8 GB VRAM, parts of the LLM can be offloaded to it. This increases the context from 32k to roughly 148k, according to huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator (paste this into its "GGUF Model URL" field: huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF/blob/main/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf), which is pretty good.

For the same money you could build a much more capable desktop PC and it would be repairable, upgradable and run cooler and probably also quieter.

Arbitrary memory size times memory speed (aka bandwidth) score:
(RAM: 136.5 GB/s = 128-bit * 8533 MT/s / 1000 / 8.
VRAM (5060 Laptop): 384 GB/s.)
7440 (= 32 GB RAM * 136.5 GB/s + 8 GB VRAM * 384 GB/s)
For comparison, a 128 GB RAM Strix Halo scores:
(RAM: 256 GB/s = 256-bit * 8000 MT/s / 1000 / 8.)
32768 (= 128 GB RAM * 256 GB/s)
The only issue with Strix Halo (Radeon 8060S) is that its prompt processing speed is that of a 4060 Laptop, so just a bit slower than what is in this Legion 7a 16 G11 laptop.

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Running/inferencing AI / LLMs requires these things:

• Memory size to fit a decently capable LLM, including memory left for context and memory speed (aka memory bandwidth).
• Prompt processing: The larger the input, the faster GPU you'd need, especially for agentic workflows, if you want things to finish in reasonable time.
• Token generation: The speed of the output generation depends on memory speed (aka memory bandwidth).

So the article was published tomorrow ?