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Shengliang Xu
3f4b95e8a4
### What does this PR do? Type of change: new feature (model recipe) Adds the NVFP4 PTQ recipe for **Qwen/Qwen3.8-2.4T-A95B** — the recipe used to produce [**nvidia/Qwen3.8-2.4T-A95B-NVFP4**](https://huggingface.co/nvidia/Qwen3.8-2.4T-A95B-NVFP4). Qwen/Qwen3.8-2.4T-A95B is a `qwen3_5_moe_text` MoE: 92 layers, 512 routed experts (top-10) plus a shared expert, with **hybrid attention** — gated-delta (linear-attention) layers interleaved with full-attention layers. It is transformers-native from >= 5.9 and its config ships `base_model_ep_plan`, so no ModelOpt plugin is required. The recipe applies: | component | precision | |---|---| | routed experts | NVFP4 (MSE-searched static weight scales, dynamic input scales) | | self-attention | FP8 (W8A8, all projections) | | linear-attention | FP8 (W8A8, the full gated-delta path — `conv1d` + all in/out projections) | | KV cache | FP8 (cast mode) | | everything else | BF16 — including MTP, left unquantized | Two things are documented in the file header because they affect how the recipe should be read: - **The full gated-delta path is FP8, and that was validated end-to-end.** The `conv1d` and the in/out projections (`in_proj_qkv` / `in_proj_z` / `in_proj_a` / `in_proj_b`, `out_proj`) are all FP8; only the norms stay BF16. `nn.Conv1d` is a registered ModelOpt quant module, so the recipe's broad `*linear_attn*` rules reach `linear_attn.conv1d` too — this is intentional and matches the published `nvidia/Qwen3.8-2.4T-A95B-NVFP4`, whose `hf_quant_config.json` lists `linear_attn.conv1d` as FP8 on every gated-delta layer (the interleaved full-attention layers have no `conv1d`). (An earlier revision of the file header / ptq.md wrongly stated the recurrent path is never quantized; corrected in this PR.) - **The source ships as native block-FP8** (`quant_method=fp8`, `weight_block_size [128,128]`, dynamic activations). The loader dequantizes it to BF16 before quantizers are inserted, so the calibrated scales are against BF16 weights, not against the shipped FP8. Filed under `modelopt_recipes/models/` per the split introduced in #2219 (per-`model_type` recipes vs model-hub checkpoint recipes); this one targets a published checkpoint, alongside `deepseek-ai/DeepSeek-V4-Pro-0813` and the Nemotron-3 entries. ### Usage ```bash # The recipe is consumed by the PTQ entrypoint the same way as the other # modelopt_recipes/models/ entries: python examples/hf_ptq/hf_ptq.py \ --pyt_ckpt_path <Qwen/Qwen3.8-2.4T-A95B checkpoint> \ --recipe models/Qwen/Qwen3.8-2.4T-A95B/ptq/nvfp4_experts_mse-fp8_self_attn-fp8_linear_attn-kv_fp8_cast \ --export_path <output> ``` ### Testing The exported checkpoint was evaluated against the BF16 baseline on **GPQA, AA-LCR, SciCode, IFBench and Terminal-Bench 2.1**, with no meaningful accuracy regression on any of them. The published `nvidia/Qwen3.8-2.4T-A95B-NVFP4` checkpoint is the artifact this recipe produces — its `hf_quant_config.json` is the ground truth for which modules are quantized (routed experts NVFP4; self-attention, all linear-attention projections **and** `conv1d`, and KV cache FP8). No new unit tests: this is a declarative recipe composed entirely of existing units (`base_disable_all`, `nvfp4`, `nvfp4_static`, `fp8`, `kv_fp8_cast`), all already covered. ### Before your PR is "*Ready for review*" - Is this change backward compatible?: ✅ (new file only; no existing behaviour touched) - 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?: N/A — declarative recipe over existing, tested units - Did you update [Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?: N/A — consistent with the recent recipe additions (#2219, #2269, #2287), which did not add entries - Did you get Claude approval on this PR?: ❌ — not yet run ### Additional Information Model card: https://huggingface.co/nvidia/Qwen3.8-2.4T-A95B-NVFP4 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Added a post-training quantization recipe for the Qwen3.8-2.4T-A95B model. * Supports MSE-searched NVFP4 quantization for routed expert layers and FP8 quantization across self-attention and gated-delta linear-attention paths. * Supports FP8 cast-mode key-value caching while retaining BF16 precision for multi-token prediction and gated-delta normalization layers. * **Documentation** * Clarified the model’s hybrid precision configuration, including FP8 treatment of the gated-delta convolution path and the scope of broad linear-attention patterns. * Documented source-checkpoint dequantization and validation behavior. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: Shengliang Xu <shengliangx@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, 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!