Asus releases 14-inch laptop with Core Ultra 7 356H, 2.8K 120 Hz OLED and 33-hour battery

[Redaktion]

Asus has released a new Vivobook Pro 14 configuration with an Intel Core Ultra 7 356H processor, 16 GB of RAM and a 512 GB SSD. The 14-inch laptop also features a 2.8K 120 Hz OLED display and claims up to 33 hours of battery life.

https://www.notebookcheck.net/Asus-releases-14-inch-laptop-with-Core-Ultra-7-356H-2-8K-120-Hz-OLED-and-33-hour-battery.1366447.0.html

[on the cusp]

up to 32 GB of LPDDR5X RAM

This is unfortunately on the cusp of being usable, because 32 GB of total memory/(V)RAM you can fit about this many context tokens of SOTA (in its class size) AI LLM model[1] huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF 4-bit UD-Q4_K_XL quant:

.. 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.

(1 token = 0.75 words)

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*(32-7-22.9) (confirms the quote)

On Windows and with the MTP variant (=faster token generation) (with MTP head: 22.9 GB (vs without MTP: 22.4 GB)), the context tokens are going to be even less than what is in the quote.

Agentic workflows often exceed 60,000 context tokens.

In this memory sense, any gaming laptop that has a 6-8 GB VRAM GPU is superior, possibly even cheaper and also going to have upgradable RAM.

Recalculating with an additional 8 GB VRAM GPU:
165,500 = 16,384*(32-7-22.9+8).

And, generally, you get more memory per buck if you get a desktop PC.

[1] artificialanalysis.ai/?models=qwen3-8-27b%2Cqwen3-8-27b-medium%2Cqwen3-8-27b-low%2Cqwen3-8-27b-non-reasoning%2Cqwen3-6-35b-a3b%2Cqwen3-6-35b-a3b-non-reasoning&intelligence=artificial-analysis-intelligence-index
[2] reddit.com/r/Qwen_AI/comments/1vo8pjz/qwen3827b_kv_cache_works_out_to_64_kibtoken_so/