[kevalmorabia97]

What does this PR do?

[2/2], after #2917 (date-prefixed announcement filenames, merged).

Type of change: documentation

Executive summary

• IQ2_S + NVFP4 matches FP8 with no training. It uses IQ2_S on the experts' gate and up projections and NVFP4 on their down projections, 3.21 bits per expert weight. The model is ~155 GB, against 328 GB for FP8 (2.1x smaller), and its six-benchmark mean is within 0.1 points of FP8.
• IQ2_S is the smallest option. At 2.56 bits per weight on all expert weights, the model is ~130 GB (2.5x smaller), and its mean is 2.3 points below FP8. Most of the loss is runaway generation on long tasks.
• QAD fixes long-context runaways. 300 iterations on IQ2_S brought AA-LCR from 70.0 to 74.0 (FP8 74.9) and cut answers hitting the token budget from 6.9% to 0.6%. The mean gap shrank from 2.3 to 1.4 points.
• We recommend the PTQ recipes. QAD cost ~4,500 GPU-hours, didn't restore knowledge recall, and IQ2_S + NVFP4 does better without training. Both recipes still trail FP8 by ~2–3 points on AA-Omniscience. We report no throughput yet, because vLLM has no IQ2 kernels.

Adds an announcement page (blog) for the GLM-5.3-Flash IQ2 study, with a results figure in the Qwen3.6 announcement's style and a card on the announcements index. It presents two PTQ recipes as alternatives:

	FP8 checkpoint	IQ2_S	IQ2_S + NVFP4
Routed experts	FP8	IQ2_S	IQ2_S gate/up, NVFP4 down
Routed-expert bits per weight	8	2.56	3.21
Model size	328 GB	~130 GB	~155 GB
Mean delta vs FP8, six benchmarks	–	−2.3	−0.1

It also covers knowledge recall (HLE, AA-Omniscience), RULER-1M, QAD on the IQ2_S model (300 iterations, −1.4, with worse recall), the cost, and the GLM-specific reproduction steps.

Blog, not a full tutorial. QAD did not become the recipe: IQ2_S + NVFP4 reaches FP8 with PTQ alone. So the page points to the Qwen3.6-35B-A3B tutorial for the general flow and lists only GLM's differences: the recipes, --no_moe_grouped_gemm, EP16 PTQ and fake-quant export, long-reasoning calibration, eval settings, and the QAD flags.

Baseline: every delta is against the FP8 release (zai-org/GLM-5.3-Flash), not the BF16 checkpoint. A note near the top says so.

Sizes: computed from each tensor's bits per weight using the FP8 checkpoint's safetensors headers. No packed IQ2 checkpoint exists, since evaluation used BF16 fake-quant exports. There is no throughput number for the same reason: vLLM has no IQ2 kernels.

Dependencies: none for merging this docs-only change. Reproducing the experiments needed:

• ModelOpt: Megatron-Bridge: GGML PTQ, QAD and fake-quant export for large MoE and VLM models #2873 (merged; GGML PTQ, QAD and fake-quant export for large MoE/VLM models) and [3/3] Add GLM-5.3-Flash support to Megatron-Bridge PTQ and HF export #2539 (GLM-5.3-Flash support for Megatron-Bridge PTQ and HF export, plus both IQ2_S recipes).
• Megatron-Bridge: feat(model): add GLM-5.3-Flash support NVIDIA-NeMo/Megatron-Bridge#6044 (GLM-5.3-Flash bridge).
• Megatron-LM: #7054 (KDA hybrid layers), #7521 (mHC precision controls), #7522 (KPool DSA indexer), plus two training fixes for QAD: ceaa6c5d (#5835, distributed-optimizer save/load for mixed fp32/bf16 param groups) and bfb17738 (#7522, freeze the DSA indexer when dsa_indexer_loss_coeff is 0).
• transformers >= 5.16.1 (glm5_next), nemo:26.10.rc2 base image, vLLM >= 0.30 for evaluation.

The recipes stay in #2539: they use Megatron-Bridge module names, and examples/hf_ptq would neither apply GPTQ to the fused HF routed experts nor match the dense-MLP and NVFP4-down patterns, so they cannot be used without the Megatron-Bridge support.

Prefix-caching check (done): our evals serve vLLM 0.31 with prefix caching on, which on vLLM < 0.31 corrupts GLM's long-context cache (vllm#57477). An A/B on FP8 AA-LCR (8 runs each) gives 74.88 ± 0.87 with caching on and 76.12 ± 0.83 with it off: about one standard error apart, so the 0.31 fix holds and the published numbers stand.

Usage

N/A (documentation)

Testing

• Parsed the new page with docutils: no errors or warnings.
• Did not run a full Sphinx build.

Before your PR is "Ready for review"

• Is this change backward compatible?: N/A
• If you copied code from any other sources or added a new PIP dependency, did you follow guidance in CONTRIBUTING.md: N/A
• Did you write any new necessary tests?: N/A
• Did you update Changelog?: N/A
• Did you get Claude approval on this PR?: ❌

Additional Information

Results come from the study's working log (examples/megatron_bridge/tutorials/GLM-5.3-Flash/WORKLOG.md on kmorabia/glm53-flash-iq2, not part of this PR).

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Summary by CodeRabbit

• Documentation
  • Added an announcement comparing GLM-5.3-Flash IQ2_S and mixed IQ2_S/NVFP4 quantization recipes against FP8, including benchmark results.
  • Documented QAD evaluation results: improved AA-LCR and fewer token-budget runaways, with no recovery in knowledge recall.
  • Included evaluation conditions, training-cost comparisons, quantization and export commands, and reproduction notes.
  • Added the announcement to the documentation homepage with related tags.

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[kevalmorabia97]

/ok to test 5d55df8

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• docs/source/announcements/20261021-glm53-flash-iq2.rst

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📝 Walkthrough

The documentation adds GLM-5.3-Flash quantization recipes, evaluation findings, QAD results, compute figures, and reproduction instructions. A new card links to the announcement from the documentation index.

Changes: GLM-5.3-Flash quantization announcement

Layer / File(s)	Summary
Quantization recipes and evaluation results docs/source/announcements/20261021-glm53-flash-iq2.rst	The announcement describes IQ2_S and IQ2_S with NVFP4 recipes. It reports model characteristics, benchmark results, knowledge-recall results, and long-context results.
QAD results and compute figures docs/source/announcements/20261021-glm53-flash-iq2.rst	The announcement documents QAD data, configuration, results across iterations, runtime measurements, and compute figures.
Reproduction instructions and announcement listing docs/source/announcements/20261021-glm53-flash-iq2.rst, docs/source/index.rst	The announcement provides PTQ, export, evaluation, and QAD instructions. The index adds a dated announcement card and topic tags.

Priority: ⬇️ Low

Estimated code review effort: 1 (Trivial) | ~5 minutes

Change: Other

Merge Risk: ⚪ Minimal · up to 04ab8

The evaluation instructions now avoid the affected vLLM 0.30 prefix-caching configuration. No actionable merge-blocking issue remains after normal checks.

Pre-merge checks: 6

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Title check		The title clearly and specifically describes the main change: documenting GLM-5.3-Flash IQ2_S PTQ recipes and QAD results.
Docstring Coverage		No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check. Docstring coverage is scoped to functions touched by this diff. Analyzed 0 functions across 0…
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Security Anti-Patterns		PASS: The pull request changes only documentation and one PNG asset. It adds no Python changes under modelopt or examples, and it changes no dependency manifests. Therefore, none of the listed sec…

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@@            Coverage Diff             @@
##             main    #2915      +/-   ##
==========================================
- Coverage   70.16%   69.89%   -0.28%     
==========================================
  Files         659      659              
  Lines       74081    75930    +1849     
==========================================
+ Hits        51979    53069    +1090     
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[kevalmorabia97]

/ok to test 13dc69c

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/ok to test b5597ec

[kevalmorabia97]

/ok to test 5ac53f1

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/ok to test fc8d081

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/ok to test 1e655cd

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/ok to test 275a9b9

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Inline comments:
Review comments at @docs/source/announcements/20261021-glm53-flash-iq2.rst:
- Around line 306-309: Update the **Evaluation** requirement to specify vLLM ≥
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[kevalmorabia97]

/ok to test c28ff05

[shengliangxu]

LGTM

[shengliangxu]

LGTM