Minima (arXiv 2609.04098, Fri 4 Sep digest, cs.AI) applies NVFP4 W4A4 to all 496 linear layers of a Qwen3.8-27B hybrid — including all 48 Gated DeltaNet layers — explicitly testing the intuition that the gating must stay high precision. Result: within noise of BF16 (average -0.52 across five tasks) on 4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench, and RULER up to 64K, while being the smallest recipe in its comparison (17.5 GiB) with the fastest prefill (+14-19%). The four-part mechanism study explains why: NVFP4's 16-element block scaling localizes outliers; gating projections are the least sensitive; the delta-rule recursion keeps noise stationary; and per-token costs amortize rather than accumulate. Quantized checkpoints are public on HF (minima-ai), along with FP8 KV-cache scaling factors.
Study quantizes a hybrid 27B model to NVFP4 W4A4, gating layers included
Minima (arXiv 2609.04098, Fri 4 Sep digest, cs.AI) applies NVFP4 W4A4 to all 496 linear layers of a Qwen3.8-27B hybrid — including all 48 Gated DeltaNet layers — explicitly testing the intuition that the gating must stay high precision. Result: within noise of BF16 (average -0.52 across five tasks) on 4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench, and RULER up to 64K, while being the smallest recipe in its comparison (17.5 GiB) with the fastest prefill (+14-19%). The four-part mechanism study explains why: NVFP4's 16-element block scaling localizes outliers; gating projections are the least sensitive; the delta-rule recursion keeps noise stationary; and per-token costs amortize rather than accumulate. Quantized checkpoints are public on HF (minima-ai), along with FP8 KV-cache scaling factors.
Imported from the existing research archive. Migration preserves the recorded judgment and is not a fresh verification.
What this means for you
The open-weight frontier just moved to hybrid linear-attention architectures (trend #1: GLM-5.3-Flash, Qwen3.8-Flash-Next), and those models have no settled quantization recipes — providers are shipping NVFP4 derivatives ad hoc. This gives the first thorough public answer for a 27B hybrid: quantize everything, including the recurrent half, and expect no measurable quality loss. Directly changes the serving-cost math for hybrid models.
Public artifacts and validated results are different.
The recorded fact is the public work and its artifacts. Author-reported outcomes do not establish independent reproduction. Next checks include usable code, task conditions and third-party validation.
Evidence & related material
Technical details & scores
| Arxiv | 2609.04098, announced in the Fri 4 Sep 2026 digest (cs.AI pool) |
|---|---|
| Target | Qwen3.8-27B hybrid: NVFP4 W4A4 on all 496 linear layers incl. all 48 Gated DeltaNet layers |
| Results | within noise of BF16 (avg -0.52 across five tasks) on 4K/32K perplexity, MMLU-Pro, GSM8K, AIME'25, GPQA-Diamond, LiveCodeBench, RULER up to 64K; smallest recipe in comparison (17.5 GiB); fastest prefill (+14-19%) |
| Mechanism | 16-element block scaling localizes outliers; gating projections least sensitive; delta-rule recursion keeps noise stationary; per-token costs amortize |
| Artifacts | quantized checkpoints on HF (minima-ai); FP8 KV-cache scaling factors released |