QuoteAlso, unexpected for a business laptop, the RAM is soldered, so while you already have 32 GB with our review device, you are also stuck with it.

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), 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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