# 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.