Qwen3.5 shrugs off 2-bit GGUFs
Benchmarking highlighted by The Kaitchup suggests Qwen3.5 397B-A17B keeps surprisingly close to its BF16 baseline even under aggressive GGUF quantization, with IQ2 looking nearly indistinguishable from Q4 on sampled runs of MMLU-Pro, GPQA Diamond, LiveCodeBench, and Math-500. The same tests showed MiniMax M2.5 degrading badly under similar quantization, making the real takeaway less “1-bit is fine” than “quant robustness is highly model-specific.”
This is the kind of result local LLM users actually need: not another perplexity chart, but a warning that quantization quality depends as much on the model as on the bit-width. It also gives Qwen a meaningful practical edge for anyone trying to squeeze frontier-class reasoning onto finite VRAM.
- –Qwen3.5 397B-A17B reportedly stays usable even at very low precision, turning sub-150GB footprints into a realistic option for serious evaluation and experimentation
- –MiniMax M2.5 collapsing under the same GGUF approach blows up the lazy rule of thumb that Q4 is always a safe default
- –The benchmarks matter because they measure generated answers on real tasks, not proxy metrics that often miss catastrophic post-quantization behavior
- –For local inference builders, quantization robustness is now part of model selection, not just a deployment afterthought
DISCOVERED
77d ago
2026-03-11
PUBLISHED
78d ago
2026-03-10
RELEVANCE
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dtdisapointingresult