---
title: INT8 W8A8
---
[](){ #int8 }

vLLM supports quantizing weights and activations to INT8 for memory savings and inference acceleration.
This quantization method is particularly useful for reducing model size while maintaining good performance.

Please visit the HF collection of [quantized INT8 checkpoints of popular LLMs ready to use with vLLM](https://huggingface.co/collections/neuralmagic/int8-llms-for-vllm-668ec32c049dca0369816415).

!!! note
    INT8 computation is supported on NVIDIA GPUs with compute capability > 7.5 (Turing, Ampere, Ada Lovelace, Hopper, Blackwell).

## Prerequisites

To use INT8 quantization with vLLM, you'll need to install the [llm-compressor](https://github.com/vllm-project/llm-compressor/) library:

```bash
pip install llmcompressor
```

Additionally, install `vllm` and `lm-evaluation-harness` for evaluation:

```bash
pip install vllm lm-eval==0.4.4
```

## Quantization Process

The quantization process involves four main steps:

1. Loading the model
2. Preparing calibration data
3. Applying quantization
4. Evaluating accuracy in vLLM

### 1. Loading the Model

Load your model and tokenizer using the standard `transformers` AutoModel classes:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, device_map="auto", torch_dtype="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
```

### 2. Preparing Calibration Data

When quantizing activations to INT8, you need sample data to estimate the activation scales.
It's best to use calibration data that closely matches your deployment data.
For a general-purpose instruction-tuned model, you can use a dataset like `ultrachat`:

??? Code

    ```python
    from datasets import load_dataset

    NUM_CALIBRATION_SAMPLES = 512
    MAX_SEQUENCE_LENGTH = 2048

    # Load and preprocess the dataset
    ds = load_dataset("HuggingFaceH4/ultrachat_200k", split="train_sft")
    ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))

    def preprocess(example):
        return {"text": tokenizer.apply_chat_template(example["messages"], tokenize=False)}
    ds = ds.map(preprocess)

    def tokenize(sample):
        return tokenizer(sample["text"], padding=False, max_length=MAX_SEQUENCE_LENGTH, truncation=True, add_special_tokens=False)
    ds = ds.map(tokenize, remove_columns=ds.column_names)
    ```

</details>

### 3. Applying Quantization

Now, apply the quantization algorithms:

??? Code

    ```python
    from llmcompressor.transformers import oneshot
    from llmcompressor.modifiers.quantization import GPTQModifier
    from llmcompressor.modifiers.smoothquant import SmoothQuantModifier

    # Configure the quantization algorithms
    recipe = [
        SmoothQuantModifier(smoothing_strength=0.8),
        GPTQModifier(targets="Linear", scheme="W8A8", ignore=["lm_head"]),
    ]

    # Apply quantization
    oneshot(
        model=model,
        dataset=ds,
        recipe=recipe,
        max_seq_length=MAX_SEQUENCE_LENGTH,
        num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    )

    # Save the compressed model: Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token
    SAVE_DIR = MODEL_ID.split("/")[1] + "-W8A8-Dynamic-Per-Token"
    model.save_pretrained(SAVE_DIR, save_compressed=True)
    tokenizer.save_pretrained(SAVE_DIR)
    ```

This process creates a W8A8 model with weights and activations quantized to 8-bit integers.

### 4. Evaluating Accuracy

After quantization, you can load and run the model in vLLM:

```python
from vllm import LLM
model = LLM("./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token")
```

To evaluate accuracy, you can use `lm_eval`:

```bash
lm_eval --model vllm \
  --model_args pretrained="./Meta-Llama-3-8B-Instruct-W8A8-Dynamic-Per-Token",add_bos_token=true \
  --tasks gsm8k \
  --num_fewshot 5 \
  --limit 250 \
  --batch_size 'auto'
```

!!! note
    Quantized models can be sensitive to the presence of the `bos` token. Make sure to include the `add_bos_token=True` argument when running evaluations.

## Best Practices

- Start with 512 samples for calibration data (increase if accuracy drops)
- Use a sequence length of 2048 as a starting point
- Employ the chat template or instruction template that the model was trained with
- If you've fine-tuned a model, consider using a sample of your training data for calibration

## Troubleshooting and Support

If you encounter any issues or have feature requests, please open an issue on the [vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor/issues) GitHub repository.
