Finally, but:
Quote$2,999.99 with the 18-core RTX Spark and 32GB RAM
3000 bucks (+ taxes on top?) / 32GB RAM/unified memory and still can't run SOTA (in its class size) AI LLM model[1] huggingface.co/unsloth/Qwen3.6-35B-A3B-MTP-GGUF at a minimum required Q4_K_XL quant with proper 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.
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[3]-22.9) (confirms the quote)
Agentic workflows often exceed 60,000 or quite a bit more context tokens.
Recalculating with an additional 8 GB VRAM GPU:
165,500 = 16,384*(32-7-22.9+8)
So, in this memory sense, any normal gaming laptop that has a 6-8 GB VRAM GPU is superior and cheaper. It's also going to have upgradable RAM (yes, upgradable RAM is slower (5600 vs 9400), but the GPU's much faster VRAM compensates for that).
And, generally, you get more memory per buck if you get a desktop PC (and get a much cheaper iGPU-only laptop for everything else/non-AI and remote desktop into your AI 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/
[3] techpowerup.com/353395/windows-11-26h2-update-lowers-ram-consumption[/quote]
A 48 GB RAM/unified memory option is unfortunately not configurable and 64 GB is the next step from 32 GB. 64 GB is ofc better, but also more expensive.