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Keval MorabiaandClaude Opus 4.8
2fc352be2d
### What does this PR do?
Type of change: New feature
Adds **vision-language model (VLM) support** to the Megatron-Bridge
examples for both **Minitron pruning** (`prune_minitron.py`) and **PTQ**
(`quantize.py`). Only the **language model** is pruned/quantized — the
vision tower and vision→language projector are left in full precision —
and the full VLM is saved back. `hidden_size` is skipped for pruning
when it is shared with the vision→LM projector.
Supported VLMs (tested e2e): **Qwen{3,3.5}-VL** (dense; hybrid
GatedDeltaNet + gated attention) and **Gemma3-VL** (sliding/full
attention).
### Calibration (image-text)
Calibration is conditioned on real **image-text** data so the language
model's pruning importance / quantizer statistics see vision-conditioned
activations. The modality is inferred from `--calib_dataset_name`:
- an **image-text** dataset (default for VLMs,
`nemotron_vlm_dataset_v2`) drives the **full VLM forward**;
- a **text** dataset runs text-only calibration of the language model
(for text-vs-image ablations).
A shared `get_megatron_vlm_calibration_forward_loop` (built on
`megatron_prefill`) drives the full VLM forward over image-text pairs
from `vlm_dataset_utils` (`scienceqa`, `nemotron_vlm_dataset_v2`, with
config-driven subset/shard caps to bound downloads). It shards across
**data-parallel (DP)** ranks like the text loop (#1804); **context
parallelism (CP)** applies to text-only VLM calibration (the shared text
loop), not the multimodal forward — splitting the sequence would
misalign the merged vision embeddings.
### Results - Cosmos-Reason2-2B
Validated end-to-end on **Cosmos-Reason2-2B** (Qwen3-VL). Minitron NAS
prunes the language-model tower **1.72B → ~1.59B** (vision encoder +
projector frozen), top_k=1. Calibration data drives pruning importance;
image-text calibration runs the full VLM forward.
| Model | Calibration | MMLU | BLINK Rel-Depth | RealWorldQA |
|---|---|---|---|---|
| Baseline (1.72B) | — | 0.58 | 0.76 | 0.61 |
| Pruned (1.59B) | text (`nemotron-post-training-dataset-v2`) | 0.51\* |
~0.69 | ~0.57 |
| Pruned (1.59B) | image+text (`nemotron_vlm_dataset_v2`) | 0.49\* |
**0.77** | **0.61** |
\* Pruned MMLU on the 10% split (the pruning score function); baseline
MMLU is the full set. The VLM-benchmark numbers for the text row were
measured with a different text calibration set and are expected to be
similar for `nemotron-post-training-dataset-v2` (marked `~`).
> [!NOTE]
> These numbers come from short single runs on small eval splits — read
them for **high-level trends only**, not as exact values.
Takeaways: pruning the LM tower of a VLM works end-to-end. **Image-text
calibration** (this PR's feature) preserves the VLM benchmarks better
than text-only — BLINK Rel-Depth ~0.77 vs ~0.69 and RealWorldQA ~0.61 vs
~0.57, both close to the unpruned baseline (0.76 / 0.61) — which is the
motivation for calibrating on vision-conditioned activations.
### Results - Qwen3.5-9B
| Model | MMLU | MMStar |
|----------------------------|:------:|:------:|
| Qwen3.5-9B | 0.7003 | 0.6117 |
| Pruned-7B (text calib) | 0.5527 | 0.4411 |
| Pruned-7B (image+text calib) | 0.5107 | 0.3941 |
### Key changes
- `quantize.py`: quantizes the **root** model with non-LM (vision)
quantizers disabled, so the ModelOpt state lives on the root (required
by the Megatron save) while only the language model is quantized.
- `prune_minitron.py`: image-text (or text) calibration for VLM pruning
importance.
- Shared VLM calibration forward loop (`megatron_prefill`-based, unwraps
tuple outputs, DP-sharded) + `vlm_dataset_utils`.
- Tiny VLM test fixtures (Qwen3.5-VL, Gemma3-VL) with vision tokens
derived dynamically from the reference processor; VLM prune + quantize
example tests.
- README + CHANGELOG.
### Usage
```bash
# Prune the language model of a VLM (image-text calibration by default)
torchrun --nproc_per_node 2 prune_minitron.py \
--pp_size 2 \
--hf_model_name_or_path <vlm> \
--prune_target_params 3e9 \
--output_hf_path /tmp/vlm-pruned
# PTQ the language model of a VLM
torchrun --nproc_per_node 2 quantize.py \
--hf_model_name_or_path <vlm> \
--quant_cfg fp8 \
--export_megatron_path /tmp/vlm-fp8-megatron
```
### Testing
- `test_prune_minitron.py::test_prune_minitron_vlm` — Gemma3-VL,
image-text (ScienceQA) calibration; full load → prune (depth + ffn) →
save → reload.
- `test_quantize_export.py::test_quantize_vlm` — Qwen3.5-VL, text
calibration; quantize LM → save Megatron checkpoint.
- LM regression tests (`test_prune_minitron`,
`test_quantize_and_export`) unchanged and passing.
### Not in scope
- **HF unified export of a quantized VLM** is not yet supported;
`export.py` saves the Megatron checkpoint only for VLMs (tracked by a
TODO in `export.py`). The recommended path is to route the megatron→HF
quant export through Megatron-Bridge's
`AutoBridge.export_hf_weights_quant(quantization_checker, quant_fn,
quant_block_size)`, which reuses the bridge's per-model mcore↔HF mapping
— covering Qwen3.5-VL / Gemma3-VL and the vision tower/projector (left
full precision) for free — so modelopt supplies only the checker +
pack/scale fn + `hf_quant_config` (KV-cache scales need a separate
path). This avoids re-authoring per-model mappings in modelopt (cf.
#1482's Qwen3-VL-only `mcore_qwen3vl.py`).
> [!NOTE]
> Qwen3.5-VL **MoE** is not tested e2e: the Megatron-Bridge weight
conversion expects packed (`gate_up_proj`) experts that transformers'
tiny checkpoint doesn't emit. MoE pruning itself is covered by
`test_mcore_qwen35_gdn_moe_pruning`.
### Before your PR is "*Ready for review*"
- Is this change backward compatible?: ✅
- 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)?:
✅
- Did you get Claude approval on this PR?: ✅
### Additional Information
Follow-up to the GatedDeltaNet/MLA/latent-MoE pruning PR (#1747).
Rebased on `main` to pick up CP/DP calibration (#1804); the VLM
calibration loop now shards across DP ranks the same way. `hidden_size`
pruning for VLMs (requires resizing the vision projector) is left for a
future PR.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added VLM-aware Minitron pruning and post-training quantization that
target only the language-model portion, keeping the vision
tower/projector in full precision.
* Calibration now auto-selects text vs image-text datasets based on
model type, with modality validation.
* Expanded Megatron-Core CP/DP guidance and introduced a `--cp_size`
flag in quantization examples.
* **Bug Fixes**
* Improved VLM generation/prefill output handling and made vocabulary
sizing more robust for VLM wrappers.
* **Tests / Documentation**
* Updated pruning/quantization docs and refreshed/added VLM-focused
tests.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (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
- [2026/05/27] 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!