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https://github.com/NVIDIA/Model-Optimizer.git
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Chenjie Luo
fda0899e40
## Summary
- Adds three built-in PTQ recipes that express the KV-cache *cast*
variants directly in YAML, using the existing `use_constant_amax: true`
quantizer field. These are recipe equivalents of
`--kv_cache_qformat=fp8_cast` / `nvfp4_cast`:
- `general/ptq/fp8_default-fp8_cast_kv`
- `general/ptq/nvfp4_default-fp8_cast_kv`
- `general/ptq/nvfp4_default-nvfp4_cast_kv`
- Makes `--recipe` authoritative in `examples/llm_ptq/hf_ptq.py`: the
post-hoc `_set_kv_cache_constant_amax` override now only runs when
`--recipe is None`, so a recipe YAML fully determines KV-cache config
instead of being silently overridden by the default
`--kv_cache_qformat=fp8_cast`. Updated help text on both flags.
- Extends the recipe loader smoke test to cover the three new recipes.
## Motivation
Before this change, the cast variants lived only in argparse
(`_KV_CAST_FORMATS = {"fp8_cast", "nvfp4_cast"}`) and were layered on
top of any recipe-loaded config. That meant `--recipe
nvfp4_default-fp8_kv` would silently become a cast recipe due to the
`--kv_cache_qformat` default. Now the recipe is self-contained: its YAML
either sets `use_constant_amax: true` on the `*[kv]_bmm_quantizer` entry
(cast) or doesn't (data-driven calibration).
## Test plan
- [x] `pytest tests/unit/recipe/test_loader.py` — all 24 tests pass,
including the three new parametrized recipes.
- [x] Verified each new recipe round-trips through `load_recipe()` with
`use_constant_amax: True` surviving Pydantic validation on the KV entry.
- [x] End-to-end run on `/models/Qwen/Qwen3-8B` (RTX 6000 Ada, 4
samples, seq_len=128) for all three new recipes:
- After `mtq.quantize(model, recipe.quantize.model_dump(),
forward_loop=...)`, all 72 `k_bmm_quantizer` / `v_bmm_quantizer` modules
have `_use_constant_amax=True` and `_get_amax()` returns `448.0` (FP8
E4M3 max).
- Weight quantizers still calibrate from data normally (sample amax
values: q_proj=0.5508, k_proj=0.6250, v_proj=0.1689, o_proj=0.7266).
- [x] Verified the `--recipe` authoritative behavior change:
- Non-cast recipe + default `--kv_cache_qformat=fp8_cast` → KV entry
does NOT get `use_constant_amax` (no silent override).
- Cast recipe + contradictory `--kv_cache_qformat=fp8` → KV entry keeps
`use_constant_amax=True` (recipe wins).
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **Bug Fixes**
* Fixed CLI to respect KV cache quantization settings from recipe YAML
instead of overriding them.
* **New Features**
* Added three new post-training quantization recipe configurations for
FP8 and NVFP4 with optimized KV cache handling.
* **Documentation**
* Enhanced CLI help text for recipe and KV cache quantization options
with configuration examples.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: Chenjie Luo <chenjiel@nvidia.com>
NVIDIA Model Optimizer (referred to as Model Optimizer, or ModelOpt) is a library comprising state-of-the-art model optimization techniques including quantization, distillation, pruning, 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
- [2026/03/11] 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>nvcr.io/nvidia/tensorrt:<version>-py3
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! | [LLMs] [diffusers] [VLMs] [onnx] [windows] | [docs] |
| Quantization Aware Training | Refine accuracy even further with a few training steps! | [Hugging Face] | [docs] |
| Pruning | Reduce your model size and accelerate inference by removing unnecessary weights! | [General] [Megatron-Bridge] | |
| Distillation | Reduce deployment model size by teaching small models to behave like larger models! | [Megatron-Bridge] [Megatron-LM] [Hugging Face] | [docs] |
| Speculative Decoding | Train draft modules to predict extra tokens during inference! | [Megatron] [Hugging Face] | [docs] |
| Sparsity | Efficiently compress your model by storing only its non-zero parameter values and their locations | [PyTorch] | [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 Quantization | View Support Matrix |
| Diffusers Quantization | View Support Matrix |
| VLM 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 |
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.
Top Contributors
Happy optimizing!