| # 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. |
| |
