Lenovo releases new colourful desktop internationally with 120 Hz display, 32 GB RAM and Intel Wildcat Lake

[Redaktion]

Lenovo has released a new 27-inch desktop internationally. Arriving two months earlier than expected, the IdeaCentre AIO 27IWC11 combines a 120 Hz display with 32 GB of RAM, Intel Wildcat Lake processors and a choice of six colour options.

https://www.notebookcheck.net/Lenovo-releases-new-colourful-desktop-internationally-with-120-Hz-display-32-GB-RAM-and-Intel-Wildcat-Lake.1396015.0.html

[32GB total memory and AI]

32 GB RAM

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

.. 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)