I gave Qwen3.8 27B a refactor on my 16GB card and it ran slower than the 3.6 35B-A3B
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I stopped Qwen3.8 27B partway through a refactor last week and handed the job back to Qwen3.6 35B-A3B. It had gotten through maybe a third of the files by then. The MoE has done that kind of work for months on a 4060 Ti 16GB I bought for games, even with a good part of its 20-plus GB at Q4_K_M sitting in system RAM. At the same quant the 27B is about 17GB, barely over the card, and I expected the newer dense model to be the easy upgrade.
The gigabyte or two that did not fit gets read on every token, because the 27B is dense and llama.cpp runs those layers on the CPU. With --n-cpu-moe the MoE keeps far more in RAM but only reads the experts each token is routed to.
To see the 27B with nothing spilling I ran both on a bigger card on HyperAI, and after waiting a while on the downloads, the dense one was fine to work with there. That card also has several times my bandwidth, and I am not sure how much of the difference came from that.
My 16GB 4060 Ti is the 8GB card with memory chips on both sides of the board and the same 128-bit bus and 288 GB/s. The DGX Spark I have been eyeing has 128GB at around 273 GB/s. It would hold the 27B without spilling and then read it at roughly my card's speed. A 70B dense model at Q4 is around 40GB, which at that bandwidth comes out to single-digit tokens per second on paper.
I have not decided yet. If someone here has run the 3.8 27B on a Spark or a Strix Halo box, decode speed at long context is the number I would want to see.