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ZhiyuandClaude Opus 5
c37a6948db
### What does this PR do?
Type of change: New feature (fail-fast guard; behavior change on a
previously silent path)
A `quant_cfg` whose module patterns don't match the model is not an
error to `set_quantizer_by_cfg` — every pattern simply matches nothing.
The run then calibrates, exports, and hands back a checkpoint that is
silently unquantized:
```json
{"quantization": {"quant_algo": null, "kv_cache_quant_algo": "FP8", "quantized_layers": {}}}
```
Nothing in the run says so. It has only ever been caught by someone
reading the exported `hf_quant_config.json` afterwards — most recently
on Step-3.7 ([NVBug 6518665](https://nvbugspro.nvidia.com/bug/6518665),
after a full 8×B200 calibration), and before that on MiniMax-M3, where
fused-expert detection skipped the experts and an experts-only recipe
matched nothing.
`mtq.quantize` now compares the config's intent against the outcome and
raises **before calibration**:
```
RuntimeError: The quantization config asks for weight quantization but no weight quantizer was
enabled, so nothing would be quantized (3 quantizer(s) inserted). These patterns matched no
weight quantizer:
*.experts.*weight_quantizer
Either the patterns do not match this architecture's module names (check the model-specific
recipes under modelopt_recipes/huggingface/<model_type>/), or the modules holding the weights
were never converted to quantized modules (an unsupported custom module, e.g. a
trust_remote_code MoE layout).
```
Scoped to avoid false positives:
- **Only configs that ask for weight quantization** are checked (an
entry with `enable` and `weight_quantizer` in its pattern), so
activation-only and KV-cache-only configs are unaffected.
- **Intent is read from each pattern's final entry**, since `quant_cfg`
entries apply in order: a pattern that is enabled and then disabled
later asks for nothing by the end.
- **Configs refining an already-quantized model** (weight quantizers
enabled by an earlier `mtq.quantize`) are left alone.
Matching goes through `conversion._match_quantizer` — the same matcher
`set_quantizer_by_cfg` used to apply the config — so "did this pattern
match anything?" is answered exactly as the applying code would. A local
`fnmatch` diverges on the two cases that matcher handles:
`SequentialQuantizer` modules (W4A8-style list-valued `cfg`) and
fused-experts names (`..._weight_quantizers.0` normalizing to
`..._weight_quantizer`).
### Usage
No API change. A config that would previously have produced an
unquantized checkpoint now raises:
```python
mtq.quantize(model, {"quant_cfg": [
{"quantizer_name": "*", "enable": False},
{"quantizer_name": "*.experts.*weight_quantizer", "cfg": {"num_bits": 8, "axis": 0}},
]}, forward_loop) # RuntimeError if the model has no `experts` modules
```
### Testing
Seven tests in `tests/unit/torch/quantization/test_quantize_cpu.py`, one
per branch of the guard: patterns matching nothing raise; an
activation-only config still runs; weight patterns disabled by a later
entry still run; enabled-then-retracted patterns still run;
`SequentialQuantizer` (list-valued `cfg`) and fused-experts quantizer
names count as matched; and the already-quantized refinement path is
exercised. Each was checked to be non-vacuous by removing the
corresponding branch and confirming exactly that test fails.
**One existing test changed.**
`tests/gpu/torch/export/test_fsdp2_export.py` parametrized over
`NVFP4_MLP_ONLY_CFG`, but its `SmallQKVModel` has no MLP — so that case
ran the FSDP2 paths against an *unquantized* model, and the new guard
reported it (4 GPU failures on the first CI run, all `quant_config6`;
`NVFP4_OMLP_ONLY_CFG` passed because that model does have `o_proj`). The
parametrization is dropped with a comment; `NVFP4_OMLP_ONLY_CFG` keeps
the scoped-recipe coverage. **If reviewers would rather not change that
test's meaning, the alternative is to downgrade the guard to a warning —
flagging it explicitly as a decision.**
I also swept every shipped `mtq.*_CFG` against `SmallQKVModel`: only the
four MLP/experts-scoped configs raise, and the other three
(`NVFP4_EXPERTS_ONLY_CFG`, `MXFP4_MLP_WEIGHT_ONLY_CFG`,
`NVFP4_MLP_WEIGHT_ONLY_CFG`) are used elsewhere only against real MoE
models (Qwen3-MoE, gpt-oss), so no other test is affected.
Ran locally after rebasing onto current `main` (torch 2.11, transformers
5.5.4): `tests/unit/torch/quantization` + `tests/unit/recipe` — 1271
passed, 7 skipped. Full `tests/unit` (minus onnx, and puzzletron which
needs `hydra`): 2676 passed, with 4 pre-existing
`test_quant_aware_conversion.py` failures that reproduce unchanged on
clean `main`. GPU tests were not run locally (no suitable GPU); the
FSDP2 change above is reasoned from the CI failure, not re-run.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ❌ — deliberately. A config that
previously produced a `quant_algo: null` checkpoint now raises. Any such
run was already not doing what it claimed; the three scoping rules above
keep intentional non-weight quantization working.
- 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?: ✅
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ (Backward Breaking Changes)
- Did you get Claude approval on this PR?: ❌
### Additional Information
Pairs with #2202 (PTQ support for Step-3.7 MoE checkpoints), which fixes
the specific model that motivated this. Independent branches; either can
merge first.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Bug Fixes**
- Quantization now detects enabled weight-quantization patterns that do
not apply to any model weights and reports a clear validation error
before calibration.
- Broad wildcard patterns and nested quantizers are now handled
correctly.
- Overlapping patterns respect the final matching setting, including
later disabling rules.
- Existing quantized models can be refined using the parsed
configuration.
- Activation-only and explicitly disabled weight-quantization
configurations remain supported.
- Pipeline-parallel stages without targeted weights can bypass this
validation when configured to do so.
- **Documentation**
- Documented the process-wide override for bypassing unmatched
weight-quantizer validation.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding and sparsity to accelerate models.
[Input] Model Optimizer currently supports inputs of a Hugging Face, PyTorch or ONNX model.
[Optimize] Model Optimizer provides Python APIs for users to easily compose the above model optimization techniques and export an optimized quantized checkpoint. Model Optimizer is also integrated with NVIDIA Megatron-Bridge, Megatron-LM and Hugging Face Accelerate for training required inference optimization techniques.
[Export for deployment] Seamlessly integrated within the NVIDIA AI software ecosystem, the quantized checkpoint generated from Model Optimizer is ready for deployment in downstream inference frameworks like SGLang, TensorRT-LLM, TensorRT, or vLLM. The unified Hugging Face export API now supports both transformers and diffusers models.
Latest News
- [2026/08/24] BLOG: AutoQuantize: A Fast Automatic Mixed-Precision Assignment
- [2026/08/17] BLOG: Developing Nemotron 3.5 Lightning NVFP4 with QAD Using NVIDIA Model Optimizer: Learn how quantization-aware distillation recovers accuracy from aggressive NVFP4 quantization while reducing model size and increasing throughput.
- [2026/06/26] BLOG: Creating the NVIDIA Nemotron 3 Ultra NVFP4 Checkpoint with NVIDIA Model Optimizer: How we quantized Nemotron 3 Ultra (550B) to NVFP4 with Model Optimizer — up to 5.9× higher decode-heavy inference throughput than GLM-5.1 754B FP4 while matching BF16 accuracy. NVFP4 Checkpoint on Hugging Face.
- [2026/05/27] End-to-end Optimization tutorial for Nemotron-3-Nano-30B-A3B: Pruning + two-phase distillation + FP8 quantization achieving 2.6× vLLM throughput and 2.6× memory reduction.
- [2026/05/13] Puzzletron: A new algorithm for heterogeneous pruning & NAS of LLM and VLM models.
- [2026/04/15] Customer story: Domyn compresses Colosseum-355B → 260B using ModelOpt's Minitron pruning + distillation
- [2026/03/17] Customer story: Bielik.AI builds Bielik Minitron 7B (33% smaller, 50% faster, 90% quality retained) using ModelOpt's Minitron pruning + distillation
- [2026/03/11] Model Optimizer quantized Nemotron-3-Super checkpoints are available on Hugging Face for download: FP8, NVFP4. Learn more in the Nemotron 3 Super release blog. Check out how to quantize Nemotron 3 models for deployment acceleration here
- [2026/03/11] NeMo Megatron Bridge now supports Nemotron-3-Super quantization (PTQ and QAT) and export workflows using the Model Optimizer library. See the Quantization (PTQ and QAT) guide for FP8/NVFP4 quantization and HF export instructions.
- [2025/12/11] BLOG: Top 5 AI Model Optimization Techniques for Faster, Smarter Inference
- [2025/12/08] NVIDIA TensorRT Model Optimizer is now officially rebranded as NVIDIA Model Optimizer.
- [2025/10/07] BLOG: Pruning and Distilling LLMs Using NVIDIA Model Optimizer
- [2025/09/17] BLOG: An Introduction to Speculative Decoding for Reducing Latency in AI Inference
- [2025/09/11] BLOG: How Quantization Aware Training Enables Low-Precision Accuracy Recovery
- [2025/08/29] BLOG: Fine-Tuning gpt-oss for Accuracy and Performance with Quantization Aware Training
- [2025/08/01] BLOG: Optimizing LLMs for Performance and Accuracy with Post-Training Quantization
- [2025/06/24] BLOG: Introducing NVFP4 for Efficient and Accurate Low-Precision Inference
- [2025/05/14] NVIDIA TensorRT Unlocks FP4 Image Generation for NVIDIA Blackwell GeForce RTX 50 Series GPUs
- [2025/04/21] Adobe optimized deployment using Model-Optimizer + TensorRT leading to a 60% reduction in diffusion latency, a 40% reduction in total cost of ownership
- [2025/04/05] NVIDIA Accelerates Inference on Meta Llama 4 Scout and Maverick. Check out how to quantize Llama4 for deployment acceleration here
- [2025/03/18] World's Fastest DeepSeek-R1 Inference with Blackwell FP4 & Increasing Image Generation Efficiency on Blackwell
- [2025/02/25] Model Optimizer quantized NVFP4 models available on Hugging Face for download: DeepSeek-R1-FP4, Llama-3.3-70B-Instruct-FP4, Llama-3.1-405B-Instruct-FP4
- [2025/01/28] Model Optimizer has added support for NVFP4. Check out an example of NVFP4 PTQ here.
- [2025/01/28] Model Optimizer is now open source!
Previous News
- [2024/10/23] Model Optimizer quantized FP8 Llama-3.1 Instruct models available on Hugging Face for download: 8B, 70B, 405B.
- [2024/09/10] Post-Training Quantization of LLMs with NVIDIA NeMo and Model Optimizer.
- [2024/08/28] Boosting Llama 3.1 405B Performance up to 44% with Model Optimizer on NVIDIA H200 GPUs
- [2024/08/28] Up to 1.9X Higher Llama 3.1 Performance with Medusa
- [2024/08/15] New features in recent releases: Cache Diffusion, QLoRA workflow with NVIDIA NeMo, and more. Check out our blog for details.
- [2024/06/03] Model Optimizer now has an experimental feature to deploy to vLLM as part of our effort to support popular deployment frameworks. Check out the workflow here
- [2024/05/08] Announcement: Model Optimizer Now Formally Available to Further Accelerate GenAI Inference Performance
- [2024/03/27] Model Optimizer supercharges TensorRT-LLM to set MLPerf LLM inference records
- [2024/03/18] GTC Session: Optimize Generative AI Inference with Quantization in TensorRT-LLM and TensorRT
- [2024/03/07] Model Optimizer's 8-bit Post-Training Quantization enables TensorRT to accelerate Stable Diffusion to nearly 2x faster
- [2024/02/01] Speed up inference with Model Optimizer quantization techniques in TRT-LLM
Install
To install stable release packages for Model Optimizer with pip from PyPI:
pip install -U nvidia-modelopt[all]
Model Optimizer will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.
To install from source in editable mode with all development dependencies or to use the latest features, run:
# Clone the Model Optimizer repository
git clone git@github.com:NVIDIA/Model-Optimizer.git
cd Model-Optimizer
pip install -e .[dev]
You can also directly use NVIDIA container images, which have Model Optimizer pre-installed:
nvcr.io/nvidia/pytorch:<version>-py3nvcr.io/nvidia/nemo:<version>nvcr.io/nvidia/tensorrt-llm/release:<version>
Before pulling and using the container images, please review their respective license terms. Make sure to upgrade Model Optimizer to the latest version as described above. Visit our installation guide for more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.
Techniques
| Technique | Description | Examples | Docs |
|---|---|---|---|
| Post Training Quantization | Compress model size by 2x-4x, speeding up inference while preserving model quality! | [HF LLMs / VLMs] [Megatron-Bridge LLMs / VLMs] [Diffusers] [ONNX] [Windows] | [docs] |
| Quantization Aware Training / Distillation | Refine accuracy of quantized models even further with a few training steps! | [Hugging Face] [Megatron-Bridge] | [docs] |
| Pruning | Reduce your model parameters or memory footprint and accelerate inference by removing unnecessary weights! | [General] [Megatron-Bridge] | |
| Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [Hugging Face] [Megatron-Bridge] [Megatron-LM] | [docs] |
| Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Hugging Face] [Megatron-LM] | [docs] |
| Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [Hugging Face] | [docs] |
Pre-Quantized Checkpoints
- Ready-to-deploy checkpoints [🤗 Hugging Face - Nvidia Model Optimizer Collection]
- Deployable on TensorRT-LLM, vLLM and SGLang
- More models coming soon!
Resources
Model Support Matrix
| Model Type | Support Matrix |
|---|---|
| LLM / VLM Quantization | View Support Matrix |
| Diffusers Quantization | View Support Matrix |
| ONNX Quantization | View Support Matrix |
| Windows Quantization | View Support Matrix |
| Quantization Aware Training | View Support Matrix |
| Pruning | View Support Matrix |
| Distillation | View Support Matrix |
| Speculative Decoding | View Support Matrix |
Deprecation Policy
Model Optimizer follows a structured approach to managing deprecated features:
- Communication: Deprecation notices are documented in the Changelog. Deprecated items include source code statements indicating deprecation timing, with runtime warnings issued upon use.
- Migration Period: Since Model Optimizer is still pre-1.0, we provide a 1-release (~1-month) migration period after deprecation. During this window, deprecated features continue functioning while issuing warnings.
- Scope: The policy addresses both complete deprecations (entire APIs removed) and partial ones (specific parameters removed while methods remain).
- Removal: Following the migration period, deprecated elements are removed in alignment with semantic versioning standards, potentially including breaking changes in minor version updates while Model Optimizer remains in 0.x.
Citation
If you use NVIDIA Model Optimizer in your research, please cite it as follows:
@misc{nvidia-modelopt,
author = {{NVIDIA Corporation}},
title = {{NVIDIA Model Optimizer}},
howpublished = {\url{https://github.com/NVIDIA/Model-Optimizer}},
year = {2024--2026},
note = {GitHub repository}
}
Contributing
Model Optimizer is now open source! We welcome any feedback, feature requests and PRs. Please read our Contributing guidelines for details on how to contribute to this project.
AI Agents
ModelOpt's agent skills can be installed from this repository and used in any workspace.
Claude Code
claude plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git
claude plugin install modelopt@modelopt
Codex
codex plugin marketplace add https://github.com/NVIDIA/Model-Optimizer.git
Then open /plugins, select the modelopt marketplace, and install modelopt.
Contributors can also use the skills directly from a checkout. See the
agent tooling notes.
Top Contributors
Happy optimizing!