We observe that such degradation varies substantially across these factors: 4-bit quantization usually preserves performance, 2-bit often causes broad degradation
> Coder: a coding version with half of the experts removed. It reaches 91% of the full model's SWE-bench Verified score (measured by its authors) and fits 32 GB of RAM.
https://github.com/Niko1221/Strata#which-model-should-i-pick
27b at q4 is ~16gb
So from a raw amount of data, qwen3.8-flash-next wins easily. But flash-next is an MoE model, so it only has 6b parameters active per token, vs 27b's dense 27b per token. So 27b@q4 uses ~16gb of weights per token, and flash-next uses about 4gb of weights (125/80 * 6).
But those numbers don't really tell us anything useful, because there is an interplay between total model size and active parameters and intelligence that isn't obvious or simple.
(sizes are based on the unsloth quants, not the coder variant, but the idea holds - this isn't calculatable with simple math, you gotta test them and see)
Holds up pretty well