Most likely a huge part of the magic is keeping the ngram table on disk, as only one row from it is read per token, so there's next to no performance hit.

Qwen 3.8 Flash Next utilizes this, with 40% of its parameters being offloaded to disk, making the effective memory requirements much lower.

My daily driver is this:

https://huggingface.co/ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RC...

It takes things a step further by simply chopping off that half of experts which isn't (or rarely is) activated in coding tasks.

End result is a somewhat competent [0] coding model which runs on a 64GB machine.

[0] it still occasionally does weird stuff like replacing an entire file with just one line from its answer when you ask it a question if you don't explicitly tell it to not change anything or loop itself if the KV Cache is quantised at below Q8_0.