Literature search notes — 2026-08-10
Scope: what is already claimed/measured about Gemma QAT (Q4_0) vs post-training quantization, to position this study's contribution. Web search performed 2026-08-10; links verified at search time only (titles/summaries, not deep-read).
Vendor claims
- Google, "Gemma 4 with quantization-aware training" (official blog): QAT integrates quantization simulation into training so the 4-bit release stays close to full-precision quality, vs. naive PTQ degradation. https://blog.google/innovation-and-ai/technology/developers-tools/quantization-aware-training-gemma-4/
- Google Developers Blog, "Gemma 3 QAT Models" (prior generation, same recipe lineage): QAT Q4 approaches Q8-PTQ quality on consumer GPUs. https://developers.googleblog.com/en/gemma-3-quantized-aware-trained-state-of-the-art-ai-to-consumer-gpus/
- Gemma 4 model overview (ai.google.dev): official model cards, context declarations. https://ai.google.dev/gemma/docs/core
Community measurements
- DEV Community, "Gemma 4 QAT on a 1080 Ti": community-side accuracy measurements; notes that naive conversion of QAT checkpoints to other formats can lose part of the QAT benefit. https://dev.to/sysoft/gemma-4-qat-on-a-1080-ti-what-quantization-aware-actually-buys-and-fitting-the-12b-on-8-gb-at-2bg5
- Overview of Gemma 4 QAT (note.com/npaka): JA-language summary of the QAT release. https://note.com/npaka/n/ndeef4df16dd2?hl=en
Surveys
- "Art and Science of Quantizing Large-Scale Models" (arXiv:2409.11650): QAT-vs-PTQ taxonomy. https://arxiv.org/pdf/2409.11650
- "Resource-Efficient Language Models" (arXiv:2505.08620): quantization for inference efficiency. https://arxiv.org/pdf/2505.08620
Gap this study addresses
Published comparisons measure general-knowledge/reasoning benchmarks on raw models. We found no measurement of (a) QAT-vs-PTQ deltas under a fixed governance stack on routing/abstention-style tasks (answer-entitlement decisions rather than answer content), (b) the same contrast run at two scales/architectures (12B dense, ~26B sparse MoE) in a paired same-task design on one consumer GPU, or (c) regime-to-regime exact-output divergence rates for temperature-0 decoding. That is the niche of this study. We do NOT claim novelty for QAT-vs-PTQ quality direction in general.