Microsoft's best laptop uses an Intel CPU - Surface Laptop 15 Gen 8 Review

[Redaktion]

Microsoft's current Surface Laptop 15 for Business is available with Intel's powerful Core Ultra X CPUs, which also includes the fast Arc B390 iGPU. The result is the best laptop you can currently get from Windows, and it is also superior to the Snapdragon models for private customers.

https://www.notebookcheck.net/Microsoft-s-best-laptop-uses-an-Intel-CPU-Surface-Laptop-15-Gen-8-Review.1413537.0.html

[AI Performance 90% !?]

Our review unit only offers 16 GB RAM, but we did not experience any issues and if you keep it light with multi-tasking, this cheaper version should be fine. 32 GB would obviously be better (you can configure up to 64 GB)

AI Performance    90%

With only 16 GB of system memory and no additional GPU's VRAM: Should be more like 9%. 8533 MT/s is nice and all, but when a AI model can't fit into the system memory, then it can't fit.

The following text is for a 32 GB system memory device, not even 16 GB for the least generally usable AI text-to-text model:

With 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:

.. 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[3]-22.9) (confirms 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/
[3] techpowerup.com/353395/windows-11-26h2-update-lowers-ram-consumption

[AI Performance 90% !?]

Qwen4-27B (confirmed[1]) (unconfirmed: MoE and/or dense) will revive 32 GB system memory devices:
Based on from what we know from Qwen3.8-Flash-Next (Qwen4 preview), such a model could be roughly 20-30% smaller and still perform better. Smaller means the 30% would be streamed from the SSD with only a 5% performance penalty vs if it was streamed from the RAM[2]. Including a possible reduction in context tokens memory requirements from 16,384 to something lower (DeepSeek show the technology already used by huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash (see the "Global KV Cache Per Token (Bytes)" image)).

35B - 25% = 26.25B (confirms that a 27B MoE model may be possible at same or better performance using mentioned technology that already exists).

Let's recalculate the context tokens we'd get using from what we know from already existing 27B models:
121,000 = 16,384*(32-7-17.6).

[1] reddit.com/r/LocalLLaMA/comments/1wmxfjs/qwen_4_announced_at_apsara_conference/
[2] notebookchat.com/index.php?topic=324510.0