Backward Breaking Changes
Deprecations
Deprecate torch<2.6 support.
New Features
(Experimental) Add quantization support for custom TensorRT op in ONNX models.
Add support for Minifinetuning (MFT; https://arxiv.org/abs/2506.15702) self-corrective distillation, which enables training on small datasets with severely mitigated catastrophic forgetting.
Add tree decoding support for Megatron Eagle models.
For most VLMs, we now explicitly disable quant on the vision part so we add them to the excluded_modules during HF export.
Add support for hidden_size and num_layers pruning for Megatron Core Mamba models in mcore_gpt_minitron mode.
Backward Breaking Changes
PyTorch dependencies for modelopt.torch features are no longer optional and pip install nvidia-modelopt is now same as pip install nvidia-modelopt[torch].
Deprecations
New Features
Upgrade TensorRT-LLM dependency to 0.20.
Add new CNN QAT example to demonstrate how to use ModelOpt for QAT.
Add support for ONNX models with custom TensorRT ops in Autocast.
Add quantization aware distillation (QAD) support in llm_qat example.
Add support for BF16 in ONNX quantization.
Add per node calibration support in ONNX quantization.
ModelOpt now supports quantization of tensor-parallel sharded Huggingface transformer models. This requires transformers>=4.52.0.
Support quantization of FSDP2 wrapped models and add FSDP2 support in the llm_qat example.
Add NeMo 2 Simplified Flow examples for quantization aware training/distillation (QAT/QAD), speculative decoding, pruning & distillation.
Fix a Qwen3 MOE model export issue.
Backward Breaking Changes
torch-dist) stored with legacy version can no longer be loaded. The remedy is to load the legacy distributed checkpoint with 0.29 and store a torch checkpoint and resume with 0.31 to convert to a new format. The following changes only apply to storing and resuming distributed checkpoint.
quantizer_state of TensorQuantizer is now stored in extra_state of QuantModule where it used to be stored in the sharded modelopt_state.
The dtype and shape of amax and pre_quant_scale stored in the distributed checkpoint are now retored. Some dtype and shape are previously changed to make all decoder layers to have homogeneous structure in the checkpoint.
Togather with megatron.core-0.13, quantized model will store and resume distributed checkpoint in a heterogenous format.
This API previously accepts a list of strings of quantization format names. It was therefore limited to only pre-defined quantization formats unless through some hacks.
With this change, now user can easily use their own custom quantization formats for auto_quantize.
In addition, the quantization_formats now exclude None (indicating “do not quantize”) as a valid format because the auto_quantize internally always add “do not quantize” as an option anyway.
Model export config is refactored. The quant config in hf_quant_config.json is converted and saved to config.json. hf_quant_config.json will be deprecated soon.
Deprecations
Deprecate Python 3.9 support.
New Features
Upgrade LLM examples to use TensorRT-LLM 0.19.
Add new model support in the llm_ptq example: Qwen3 MoE.
ModelOpt now supports advanced quantization algorithms such as AWQ, SVDQuant and SmoothQuant for cpu-offloaded Huggingface models.
Add AutoCast tool to convert ONNX models to FP16 or BF16.
Add --low_memory_mode flag in the llm_ptq example support to initialize HF models with compressed weights and reduce peak memory of PTQ and quantized checkpoint export.
Support NemotronHForCausalLM, Qwen3ForCausalLM, Qwen3MoeForCausalLM Megatron Core model import/export (from/to HuggingFace).
Backward Breaking Changes
Refactor SequentialQuantizer to improve its implementation and maintainability while preserving its functionality.
Deprecations
Deprecate torch<2.4 support.
New Features
Upgrade LLM examples to use TensorRT-LLM 0.18.
Add new model support in the llm_ptq example: Gemma-3, Llama-Nemotron.
Add INT8 real quantization support.
Add an FP8 GEMM per-tensor quantization kernel for real quantization. After PTQ, you can leverage the mtq.compress API to accelerate evaluation of quantized models.
Use the shape of Pytorch parameters and buffers of TensorQuantizer to initialize them during restore. This makes quantized model restoring more robust.
Support adding new custom quantization calibration algorithms. Please refer to mtq.calibrate or custom calibration algorithm for more details.
Add EAGLE3 (LlamaForCausalLMEagle3) training and unified ModelOpt checkpoint export support for Megatron-LM.
--override_shapes flag to ONNX quantization.
--calibration_shapes is reserved for the input shapes used for calibration process.
--override_shapes is used to override the input shapes of the model with static shapes.
Add support for UNet ONNX quantization.
Enable concat_elimination pass by default to improve the performance of quantized ONNX models.
Enable Redundant Cast elimination pass by default in moq.quantize.
Add new attribute parallel_state to DynamicModule to support distributed parallelism such as data parallel and tensor parallel.
Add MXFP8, NVFP4 quantized ONNX export support.
Add new example for torch quantization to ONNX for MXFP8, NVFP4 precision.
Deprecations
Deprecate real quantization configs, please use mtq.compress API for model compression after quantization.
New Features
Add new model support in the llm_ptq example: OpenAI Whisper. Experimental support: Llama4, QwQ, Qwen MOE.
Add blockwise FP8 quantization support in unified model export.
Add quantization support to the Transformer Engine Linear module.
Add support for SVDQuant. Currently, only simulation is available; real deployment (for example, TensorRT deployment) support is coming soon.
Store modelopt_state in Megatron Core distributed checkpoint (used in NeMo and Megatron-LM) differently to support distributed checkpoint resume expert-parallel (EP). The legacy modelopt_state in the distributed checkpoint generated by previous modelopt version can still be loaded in 0.27 and 0.29 but will need to be stored in the new format.
Add triton-based NVFP4 quantization kernel that delivers approximately 40% performance improvement over the previous implementation.
Add a new API mtq.compress for model compression for weights after quantization.
Add option to simplify ONNX model before quantization is performed.
Add FP4 KV cache support for unified HF and TensorRT-LLM export.
Add speculative decoding support to Multi-Token Prediction (MTP) in Megatron Core models.
Add support for --calibration_shapes flag.
Add automatic type and shape tensor propagation for full ORT support with TensorRT EP.
Known Issues
Quantization of T5 models is broken. Please use nvidia-modelopt==0.25.0 with transformers<4.50 meanwhile.
Deprecations
Deprecate Torch 2.1 support.
Deprecate humaneval benchmark in llm_eval examples. Please use the newly added simple_eval instead.
Deprecate fp8_naive quantization format in llm_ptq examples. Please use fp8 instead.
New Features
Support fast hadamard transform in TensorQuantizer. It can be used for rotation based quantization methods, e.g. QuaRot. Users need to install the package fast_hadamard_transfrom to use this feature.
Add affine quantization support for the KV cache, resolving the low accuracy issue in models such as Qwen2.5 and Phi-3/3.5.
Add FSDP2 support. FSDP2 can now be used for QAT.
Add LiveCodeBench and Simple Evals to the llm_eval examples.
Disabled saving modelopt state in unified hf export APIs by default, i.e., added save_modelopt_state flag in export_hf_checkpoint API and by default set to False.
Add FP8 and NVFP4 real quantization support with LLM QLoRA example.
The modelopt.deploy.llm.LLM now support use the tensorrt_llm._torch.LLM backend for the quantized HuggingFace checkpoints.
Add end-to-end AutoDeploy example for AutoQuant LLM models.
Backward Breaking Changes
Support TensorRT-LLM to 0.17. Examples (e.g. benchmark task in llm_ptq) may not be fully compatible with TensorRT-LLM 0.15.
Nvidia TensorRT Model Optimizer has changed its LICENSE from NVIDIA Proprietary (library wheel) and MIT (examples) to Apache 2.0 in this first full OSS release.
Deprecate Python 3.8, Torch 2.0, and Cuda 11.x support.
ONNX Runtime dependency upgraded to 1.20 which no longer supports Python 3.9.
In the Huggingface examples, the trust_remote_code is by default set to false and require users to explicitly turning it on with --trust_remote_code flag.
New Features
Added OCP Microscaling Formats (MX) for fake quantization support, including FP8 (E5M2, E4M3), FP6 (E3M2, E2M3), FP4, INT8.
Added NVFP4 quantization support for NVIDIA Blackwell GPUs along with updated examples.
Allows export lm_head quantized TensorRT-LLM checkpoint. Quantize lm_head could benefit smaller sized models at a potential cost of additional accuracy loss.
TensorRT-LLM now supports Moe FP8 and w4a8_awq inference on SM89 (Ada) GPUs.
New models support in the llm_ptq example: Llama 3.3, Phi 4.
Added Minitron pruning support for NeMo 2.0 GPT models.
Exclude modules in TensorRT-LLM export configs are now wildcards
The unified llama3.1 FP8 huggingface checkpoints can be deployed on SGLang.
Backward Breaking Changes
Support TensorRT-LLM to 0.15. Examples (e.g. benchmark task in llm_ptq) may not be fully compatible with TensorRT-LLM 0.14.
Remove the deprecated arg export_npz from the mt.export.export_tensorrt_llm_checkpoint API
Deprecate mt.export.export_to_vllm API for mt.export.export_hf_checkpoint
Rename decoder type gptnext to gpt in llm_ptq to align with TensorRT-LLM model definition.
New Features
Added new tutorial notebooks for Minitron pruning and distillation in NVIDIA NeMo framework.
New models support in the llm_ptq example: Minitron, Phi3.5 MOE.
New models support in the vlm_ptq example: Llama3.2(Mllama)
mt.export.export_tensorrt_llm_checkpoint and mt.export.export_hf_checkpoint no longer requires the dtype arg.
Added an example to deploy and run quantized fp8 llama3.1 8B instruct model from HuggingFace modelopt model hub on both TensorRT and vLLM.
Bug Fixes
Improve Minitron pruning quality by avoiding possible bf16 overflow in importance calculation and minor change in hidden_size importance ranking.
Misc
Added deprecation warnings for Python 3.8, torch 2.0, and CUDA 11.x. Support will be dropped in the next release.
Backward Breaking Changes
Deprecated the summarize task in the llm_ptq example.
Deprecated the type flag in the huggingface_example.sh
Deprecated Python plugin support in ONNX.
Support TensorRT-LLM 0.13. Examples not compatible with TensorRT-LLM 0.12.
mtq.auto_quantize API has been updated. The API now accepts forward_step and forward_backward_step as arguments instead of loss_func and collect_func. Please see the API documentation for more details.
New Features
ModelOpt is compatible for SBSA aarch64 (e.g. GH200) now! Except ONNX PTQ with plugins is not supported.
Add effective_bits as a constraint for mtq.auto_qauntize.
lm_evaluation_harness is fully integrated to modelopt backed by TensorRT-LLM. lm_evaluation_harness benchmarks are now available in the examples for LLM accuracy evaluation.
A new --perf flag is introduced in the modelopt_to_tensorrt_llm.py example to build engines with max perf.
Users can choose the execution provider to run the calibration in ONNX quantization.
Added automatic detection of custom ops in ONNX models using TensorRT plugins. This requires the tensorrt python package to be installed.
Replaced jax with cupy for faster INT4 ONNX quantization.
mtq.auto_quantize now supports search based automatic quantization for NeMo & MCore models (in addition to HuggingFace models).
Add num_layers and hidden_size pruning support for NeMo / Megatron-core models.
Backward Breaking Changes
Deprecated torch<2.0 support.
modelopt.torch.utils.dataset_utils.get_dataset_dataloader() now returns a key value pair instead of the tensor.
New Features
New APIs and examples: modelopt.torch.prune for pruning Conv, Linear, and Attention heads for NVIDIA Megatron-core GPT-style models (e.g. Llama 3), PyTorch Computer Vision models, and HuggingFace Bert/GPT-J models.
New API: modelopt.torch.distill for knowledge distillation, along with guides and example.
New Example: HF BERT Prune, Distill & Quantize showcasing how to chain pruning, distillation, and quantization to achieve the best performance on a given model.
Added INT8/FP8 DQ-only support for ONNX model.
New API: modelopt.torch.speculative for end-to-end support of Medusa models.
Added Medusa QAT and End-to-end examples.
Modelopt now supports automatic save/restore of modelopt_state with the .save_pretrained and .from_pretrained APIs from Huggingface libraries, such as transformers and diffusers. This feature can be enabled by calling mto.enable_huggingface_checkpointing().
ONNX FP8 quantization support with amax calibration.
TensorRT-LLM dependency upgraded to 0.12.0. Huggingface tokenizer files are now also stored in the engine dir.
The unified model export API modelopt.torch.export.export_hf_checkpoint supports exporting fp8 and int4_awq quantized checkpoints with packed weights for Hugging Face models with namings aligned with its original checkpoints. The exported fp8 checkpoints can be deployed with both TensorRT-LLM and VLLM.
Add int8 and fp8 quantization support for the FLUX.1-dev model.
Add a Python-friendly TensorRT inference pipeline for diffusion models.
Misc
Added deprecation warning for set_data_parallel_group and set_tensor_parallel_group. These APIs are no longer needed for supporting distributed data and tensor parallelism in quantization. They will be removed in a future release.
Backward Breaking Changes
Deprecated QuantDescriptor. Use QuantizerAttributeConfig to configure TensorQuantizer. set_from_attribute_config can be used to set the quantizer attributes from the config class or attribute dictionary. This change applies only to backend APIs. The change is backward compatible if you are using only the mtq.quantize API.
New Features
Added quantization support for torch RNN, LSTM, GRU modules. Only available for torch>=2.0.
modelopt.torch.quantization now supports module class based quantizer attribute setting for mtq.quantize API.
Added new LLM PTQ example for DBRX model.
Added new LLM (Gemma 2) PTQ and TensorRT-LLM checkpoint export support.
Added new LLM QAT example for NVIDIA NeMo framework.
TensorRT-LLM dependency upgraded to 0.11.0.
(Experimental): mtq.auto_quantize API which quantizes a model by searching for the best per-layer quantization formats.
(Experimental): Added new LLM QLoRA example with NF4 and INT4_AWQ quantization.
(Experimental): modelopt.torch.export now supports exporting quantized checkpoints with packed weights for Hugging Face models with namings aligned with its original checkpoints.
(Experimental) Added support for quantization of ONNX models with TensorRT plugin.
Misc
Added deprecation warning for torch<2.0. Support will be dropped in next release.
Backward Breaking Changes
PTQ examples have been upgraded to use TensorRT-LLM 0.10.
New Features
Adding TensorRT-LLM checkpoint export support for Medusa decoding (official MedusaModel and Megatron Core GPTModel).
Enable support for mixtral, recurrentgemma, starcoder, qwen in PTQ examples.
Adding TensorRT-LLM checkpoint export and engine building support for sparse models.
Import scales from TensorRT calibration cache and use them for quantization.
(Experimental) Enable low GPU memory FP8 calibration for the Hugging Face models when the original model size does not fit into the GPU memory.
(Experimental) Support exporting FP8 calibrated model to VLLM deployment.
(Experimental) Python 3.12 support added.
Backward Breaking Changes
[!!!] The package was renamed from ammo to modelopt. The new full product name is Nvidia TensorRT Model Optimizer. PLEASE CHANGE ALL YOUR REFERENCES FROM ammo to modelopt including any paths and links!
Default installation pip install nvidia-modelopt will now only install minimal core dependencies. Following optional dependencies are available depending on the features that are being used: [deploy], [onnx], [torch], [hf]. To install all dependencies, use pip install "nvidia-modelopt[all]".
Deprecated inference_gpus arg in modelopt.torch.export.model_config_export.torch_to_tensorrt_llm_checkpoint. User should use inference_tensor_parallel instead.
Experimental modelopt.torch.deploy module is now available as modelopt.torch._deploy.
New Features
modelopt.torch.sparsity now supports sparsity-aware training (SAT). Both SAT and post-training sparsification supports chaining with other modes, e.g. SAT + QAT.
modelopt.torch.quantization natively support distributed data and tensor parallelism while estimating quantization parameters. The data and tensor parallel groups needs to be registered with modelopt.torch.utils.distributed.set_data_parallel_group and modelopt.torch.utils.distributed.set_tensor_parallel_group APIs. By default, the data parallel group is set as the default distributed group and the tensor parallel group is disabled.
modelopt.torch.opt now supports chaining multiple optimization techniques that each require modifications to the same model, e.g., you can now sparsify and quantize a model at the same time.
modelopt.onnx.quantization supports FLOAT8 quantization format with Distribution calibration algorithm.
Native support of modelopt.torch.opt with FSDP (Fully Sharded Data Parallel) for torch>=2.1. This includes sparsity, quantization, and any other model modification & optimization.
Added FP8 ONNX quantization support in modelopt.onnx.quantization.
Added Windows (win_amd64) support for ModelOpt released wheels. Currently supported for modelopt.onnx submodule only.
Bug Fixes
Fixed the compatibility issue of modelopt.torch.sparsity with FSDP.
Fixed an issue in dynamic dim handling in modelopt.onnx.quantization with random calibration data.
Fixed graph node naming issue after opset conversion operation.
Fixed an issue in negative dim handling like dynamic dim in modelopt.onnx.quantization with random calibration data.
Fixed allowing to accept .pb file for input file.
Fixed copy extra data to tmp folder issue for ONNX PTQ.
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