More Qwen MoE capacity per GPU, without giving up model quality.
BBQ-FP4 reduces model-weight memory by 3.2x to 3.8x while matching or exceeding full-precision average benchmark recovery.
3.2x – 3.8x
smaller model-weight footprint
16 GB vs 57 GB on Qwen3-30B-A3B
40 GB vs 152 GB on Qwen3-Next-80B-A3B
101.4% / 101.3%
average recovery
Across the two Qwen MoE models
versus bf16 reference
up to 2.67x
decode throughput
6,151 tok/s on Qwen3-30B-A3B
vs 2,304 tok/s in bf16
Benchmark evidence: BBQ vs. external 4-bit baseline
Both comparison models use approximately the same 4-bit footprint. Higher recovery and lower KLD indicate a closer match to bf16 behavior.
What this means for cloud providers
Increase deployable model capacity within existing GPU memory envelopes. The same compression class becomes a quality differentiator, not just a cost lever.
Where BBQ is strongest
Quality-sensitive MoE serving where benchmark regressions, distribution drift, and GPU memory ceilings matter as much as raw model compression.
Methodology: Post-training FP4 quantization of Qwen3 MoE models. Recovery and memory savings are relative to bf16. External comparison values marked ~ are extrapolated, not directly benchmarked. Standard benchmarks are proxies and may not represent all production workloads.
* Qwen3-30B-A3B worst-case recovery: BBQ-FP4-S; BBQ-FP4-L achieved 100.7%.