Quote from: 48GB AI config available? on Today at 11:19:01In this memory sense, any upgradable RAM

32 GB RAM, but the additional 8 GB VRAM make the crucial difference of 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 at good speeds possible.

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).

2500 bucks for soldered 32 GB RAM and no dGPU..

Quote32 GB of 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 (fewer is better, if everything remains the same) -> 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:

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.

In this memory sense, any upgradable RAM + 6-8 GB VRAM generic gaming laptop (used for 700-800 bucks) is superior and much cheaper, even if new.

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 parameters get activated per generated token) scores much higher, but it's much slower) (you can look up non-reasoning scores or other models in the table)