NVIDIA Model Optimizer

The NVIDIA Model Optimizer is a library designed to optimize models for inference with NVIDIA GPUs. It includes tools for Post-Training Quantization (PTQ) and Quantization Aware Training (QAT) of Large Language Models (LLMs), Vision Language Models (VLMs), and diffusion models.

We recommend installing the library with:

bash

pip install nvidia-modelopt

Supported ModelOpt checkpoint formats

vLLM detects ModelOpt checkpoints via hf_quant_config.json and supports the following quantization.quant_algo values:

FP8: per-tensor weight scale (+ optional static activation scale).
FP8_PER_CHANNEL_PER_TOKEN: per-channel weight scale and dynamic per-token activation quantization.
FP8_PB_WO (ModelOpt may emit fp8_pb_wo): block-scaled FP8 weight-only (typically 128×128 blocks).
NVFP4: ModelOpt NVFP4 checkpoints (use quantization="modelopt_fp4").
MXFP8: ModelOpt MXFP8 checkpoints (use quantization="modelopt_mxfp8").

Quantizing HuggingFace Models with PTQ

You can quantize HuggingFace models using the example scripts provided in the Model Optimizer repository. The primary script for LLM PTQ is typically found within the examples/llm_ptq directory.

Below is an example showing how to quantize a model using modelopt's PTQ API:

??? code

```python
import modelopt.torch.quantization as mtq
from transformers import AutoModelForCausalLM

# Load the model from HuggingFace
model = AutoModelForCausalLM.from_pretrained("<path_or_model_id>")

# Select the quantization config, for example, FP8
config = mtq.FP8_DEFAULT_CFG

# Define a forward loop function for calibration
def forward_loop(model):
    for data in calib_set:
        model(data)

# PTQ with in-place replacement of quantized modules
model = mtq.quantize(model, config, forward_loop)
```

After the model is quantized, you can export it to a quantized checkpoint using the export API:

python

import torch
from modelopt.torch.export import export_hf_checkpoint

with torch.inference_mode():
    export_hf_checkpoint(
        model,  # The quantized model.
        export_dir,  # The directory where the exported files will be stored.
    )

The quantized checkpoint can then be deployed with vLLM. As an example, the following code shows how to deploy nvidia/Llama-3.1-8B-Instruct-FP8, which is the FP8 quantized checkpoint derived from meta-llama/Llama-3.1-8B-Instruct, using vLLM:

??? code

```python
from vllm import LLM, SamplingParams

def main():
    model_id = "nvidia/Llama-3.1-8B-Instruct-FP8"

    # Ensure you specify quantization="modelopt" when loading the modelopt checkpoint
    llm = LLM(model=model_id, quantization="modelopt", trust_remote_code=True)

    sampling_params = SamplingParams(temperature=0.8, top_p=0.9)

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]

    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

if __name__ == "__main__":
    main()
```

Running the OpenAI-compatible server

To serve a local ModelOpt checkpoint via the OpenAI-compatible API:

bash

vllm serve <path_to_exported_checkpoint> \
  --quantization modelopt \
  --host 0.0.0.0 --port 8000

Testing (local checkpoints)

vLLM's ModelOpt unit tests are gated by local checkpoint paths and are skipped by default in CI. To run the tests locally:

bash

export VLLM_TEST_MODELOPT_FP8_PC_PT_MODEL_PATH=<path_to_fp8_pc_pt_checkpoint>
export VLLM_TEST_MODELOPT_FP8_PB_WO_MODEL_PATH=<path_to_fp8_pb_wo_checkpoint>
pytest -q tests/quantization/test_modelopt.py