Asus releases new 14-inch gaming laptop with 48 GB VRAM and 165 Hz display

[Redaktion]

Asus has started selling its first 14-inch gaming laptop backed by AMD's Strix Halo architecture. Available in Europe, China, and Japan, the new gaming laptop can assign up to 48 GB of VRAM to its Radeon 8060S iGPU thanks to its built-in 64 GB of RAM.

https://www.notebookcheck.net/Asus-releases-new-14-inch-gaming-laptop-with-48-GB-VRAM-and-165-Hz-display.1209900.0.html

[Hegarmin]

Literally not worth it at that price

[Buggi]

I was superhyped for this, now I'm thinking of getting the old one instead.

[48 and 64 GB RAM nice]

48 GB RAM fits and runs LLMs like GLM-4.7-Flash at solid q8 quant (32 GB) (or Gpt-Oss-20B (14 GB), but this would also fit within 32 GB RAM).
Unfortunately 64 GB RAM won't fit Gpt-Oss-120B even the smallest quant (62.6 GB), but 96 GB RAM would fit it easily with a lot (or even full) of context (go to huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator, paste huggingface.co/unsloth/gpt-oss-120b-GGUF/blob/main/gpt-oss-120b-F16.gguf into the calculator and set context to full).

[VPD]

I mean, yeah. You can get the rtx 5050 version of this laptop for £1049. I don't think it's worth paying almost double for strix halo.

[drive-by poster]

so if gaming is your use case I suspect you have to be an AMD fanboy to justify it.

If AI is your use case it is a "Shut up and take my money" offering. I just bought a Strix Halo 128GB desktop and I think I could live with 64GB for a lot of what I am doing.

I would buy this in a heartbeat, if I were unwilling to have a desktop.

[Haunter]

48 GB RAM fits and runs LLMs like GLM-4.7-Flash at solid q8 quant (32 GB) (or Gpt-Oss-20B (14 GB), but this would also fit within 32 GB RAM).
Unfortunately 64 GB RAM won't fit Gpt-Oss-120B even the smallest quant (62.6 GB), but 96 GB RAM would fit it easily with a lot (or even full) of context (go to huggingface.co/spaces/oobabooga/accurate-gguf-vram-calculator, paste huggingface.co/unsloth/gpt-oss-120b-GGUF/blob/main/gpt-oss-120b-F16.gguf into the calculator and set context to full).

Well, you will run LLM on NPU. Not iGPU lmao. I bought laptop with Ryzen AI 350 and normal SO-DIMM, upgraded to 64GB and I can now use NPU with 32GB. It's faster, than iGPU, but you must use models optimized for it. Right now I'm playing with converting qwen3 coder to onnx, int8 or int4 (NPU has int8 HW optimization).

[48 and 64 GB RAM nice]

Well, you will run LLM on NPU. Not iGPU lmao.

It's the other way around. Big, popular, SOTA LLMs (that you can run using llama.cpp' WebUI and download the models straight from huggingface.co) are not run by the NPU at all, and it's not even supported on Linux e.g., but on GPU (/ iGPU) and memory. A NPU indeed requires specialized LLMs (like the ones built-in Windows 11), which makes a NPU almost useless so far (not saying that it may become a popular, power efficient, accelerator).

A NPU is never mentioned if you read the comments, it's all about memory size, memory bandwidth and the usually, out of it, resulting GPU performance, that are the key hardware requirements (of course, software support, like CUDA, mainly for training, too).

Sorry, but maybe you want to cope that you bought a more expensive and in this sense unnecessary Ryzen AI platform. Unless you can prove that a NPU is worth it using benchmarks (pp, tg, power efficiency and LLM performance).

Faster in what? prompt processing or token generation. Do a benchmark from like 1k to 64k (if it fits) of NPU vs iGPU. Maybe a NPU can be added with the iGPU, still, need to see where a NPU is actually useful, other than blurring the webcam background or fake where the eyes are looking at, more power efficiently (power efficiently matters a lot, but these use-cases are rather niche so far).