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Chenjie LuoandClaude Opus 4.8
23be82655b
### What does this PR do?
Type of change: new feature
Adds `nvfp4_act_headroom`, a calibration algorithm for NVFP4
**activation** global scales.
NVFP4 scales a tensor in two levels: an FP8-E4M3 scale per 16-element
block, plus one per-tensor global scale. Plain `max` calibration sets
that global scale from the largest per-block amax seen during
calibration, which leaves no room above it — any activation larger than
the calibration max saturates.
`nvfp4_act_headroom` instead anchors the global scale to a low
percentile of the per-block amax distribution:
```
amax = max(rho * anchor, floor)
```
`anchor` is the per-block amax at `anchor_percentile`; `upper` is the
per-block amax at `upper_percentile` and is the top of the range the
scale commits to representing. Multiplying the low anchor by `rho`
places the calibrated blocks in the lower part of the FP8 scale range
and leaves the rest as headroom.
`upper_percentile` defaults to 99.99 rather than the literal maximum on
purpose. Flooring at the literal max means one freak block drags the
global scale up until every other block's FP8 block scale falls below
subnormal: on a tensor with a single block seven orders of magnitude
above the rest, that flushes 99.998% of elements to zero, versus 6.7%
when the rare blocks are clipped instead. On a benign wide-range tensor
the two choices differ by 0.4% relative MSE. Set `upper_percentile=100`
to floor at the literal observed max, which guarantees no calibration
data is clipped, at that exposure. The calibrator warns when the
per-block range is too wide for `rho` to clear any headroom.
The algorithm applies only to NVFP4 dynamic-block **input** quantizers;
weight quantizers and everything else keep plain `max`.
Files:
- `calib/nvfp4_act_headroom.py` — `NVFP4ActHeadroomCalibrator` (log2
histogram of per-block amaxes, bounded memory)
- `config.py` — `NVFP4ActHeadroomCalibConfig` (`anchor_percentile`
default 1, `rho` default 16384)
- `model_calib.py` — `nvfp4_act_headroom_calibrate`
- `mode.py` — mode registration
- `modelopt_recipes/general/ptq/nvfp4_act_headroom-kv_fp8_cast.yaml` —
mirrors `nvfp4_default-kv_fp8_cast` (dynamic NVFP4 W4A4 + FP8 KV-cache
cast, same module coverage) with only the calibration algorithm swapped:
```diff
- algorithm: max
+ algorithm:
+ method: nvfp4_act_headroom
+ anchor_percentile: 1
```
### Why a dedicated collector rather than reusing `HistogramCalibrator`
`HistogramCalibrator` histograms the tensor values themselves into
linear, dynamically re-ranged bins, and its percentile mode returns an
amax that *clips* a high-tail fraction. This algorithm needs a different
statistic (per-block amaxes, a derived quantity), different bin spacing
(log2, because block amaxes span many decades and the anchor is read
from the **low** tail, where linear bins have almost no resolution), and
a different reduction (a low-percentile anchor scaled up, not an
upper-tail clip). Reusing it would mean changing its collection
semantics for every existing caller; composing it would still leave the
log-spacing and the derived statistic unaddressed. The collector here is
a fixed 512-bin int64 histogram per quantizer, so the memory argument
for a histogram (rather than retaining values) is preserved.
### Usage
```bash
python examples/hf_ptq/hf_ptq.py --pyt_ckpt_path <model> \
--recipe general/ptq/nvfp4_act_headroom-kv_fp8_cast --dataset <calib.jsonl>
```
Or directly:
```python
import modelopt.torch.quantization as mtq
NVFP4 = {"num_bits": (2, 1), "block_sizes": {-1: 16, "type": "dynamic", "scale_bits": (4, 3)}}
config = {
"quant_cfg": [
{"quantizer_name": "*", "enable": False},
{"quantizer_name": "*weight_quantizer", "cfg": NVFP4},
{"quantizer_name": "*input_quantizer", "cfg": NVFP4},
],
"algorithm": {"method": "nvfp4_act_headroom", "anchor_percentile": 1, "rho": 16384},
}
model = mtq.quantize(model, config, forward_loop)
```
### Testing
**Unit tests** —
`tests/unit/torch/quantization/test_nvfp4_act_headroom.py`, 26 tests
covering the anchor and range terms, monotonicity in
`anchor_percentile`, headroom above plain max, the too-wide-range
fallback and its warning, the rare-outlier case (the default clips it;
`upper_percentile=100` chases it), the `upper_percentile=100`
no-clipping guarantee across six distributions, input validation (`rho`
/ percentile bounds, NaN/Inf, all-zero, a last dim that is not a
multiple of the block size), quantizer selection, calibrator restoration
after calibration, `shared_states` / `sync_expert_weight_amax`
forwarding, and W4A4 end-to-end confirming weights fall back to plain
max while only activations get the headroom scale.
Full `tests/unit/torch/quantization/` and `tests/unit/recipe/` suites
pass with no regressions (the remaining failures in this environment are
a pre-existing broken-`torchvision` import and
`test_data_parallel_auto_quantize`, both of which fail identically on
`main`).
**End-to-end on a real model** — ran the shipped recipe on a 30B hybrid
Mamba/attention MoE (1024 calibration samples @ seq 4096) and verified
the export is a standard NVFP4 checkpoint: 6268 activation quantizers
calibrated; 19G vs the 62G BF16 source; 6260 `input_scale`, 6267
`weight_scale` / `weight_scale_2`; `quant_algo: NVFP4`,
`kv_cache_quant_algo: FP8`; 53 excluded modules; no module carrying an
`input_scale` without a `weight_scale`. 32 of 6268 quantizers (0.5%) had
a per-block range too wide for `rho` to clear and warned.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅ — purely additive: a new opt-in
algorithm, config class, and recipe. No existing behavior changes.
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A — no
copied code, no new dependencies.
- Did you write any new necessary tests?: ✅ — 12 new unit tests.
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
✅ — entry under 0.47 → New Features → Quantization.
- Did you get Claude approval on this PR?: ❌ — not yet run.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
---------
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
Co-authored-by: Claude Opus 4.8 <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
- [2026/06/26] 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
For AI-assisted development setup, see the agent tooling notes.
Top Contributors
Happy optimizing!