QuoteIts configuration selector currently offers a Ryzen 5 220 model with 16 GB of RAM and a 256 GB SSD, alongside a Core Ultra 5 125U version with 16 GB of RAM and 512 GB of storage.
And this makes it pretty much completely unsuitable for AI (unless you are ok with running relatively small (and incapable) AI models, but which are fine for your use-case):
Even 32 GB of RAM/total memory would allow only for a limited context:
With only 32 GB of total memory 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:
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.
(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 (MTP heads require a bit more space (advantage: faster token generation, disadvantage: slower prompt procesing)), 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 and possibly even cheaper. It's 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/
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