---
language:
- multilingual
base_model:
- Qwen/Qwen3-8B
pipeline_tag: text-generation
tags:
- qwen
- qwen3
- fp8
- vllm
- conversational
- text-generation-inference
- llm-compressor
license: apache-2.0
---

## Model Overview
- **Model Architecture:** Qwen3ForCausalLM
  - **Input:** Text
  - **Output:** Text
- **Model Optimizations:**
  - **Activation quantization:** FP8
  - **Weight quantization:** FP8
- **Intended Use Cases:** Intended for commercial and research use. Similarly to the base model, this quantized version is intended for assistant-like chat and multilingual text generation across 100+ languages.
- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
- **Version:** 1.0
- **Model Developers:** RedHat (Neural Magic)

### Model Optimizations

This model was obtained by quantizing activations and weights of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) to FP8 data type.
This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
Weight quantization also reduces disk size requirements by approximately 50%.

Only weights and activations of the linear operators within transformers blocks are quantized.
Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.

## Deployment

This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.

```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "inference-optimization/Qwen3-8B-FP8-Dynamic"
number_gpus = 1
sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
```

## Creation

<details>
  <summary>Creation details</summary>

  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.

  ```python
from compressed_tensors.offload import dispatch_model
from transformers import AutoModelForCausalLM, AutoTokenizer

from llmcompressor import oneshot
from llmcompressor.modifiers.quantization import QuantizationModifier

MODEL_ID = "Qwen/Qwen3-8B"

# Load model.
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

recipe = QuantizationModifier(
    targets=["Linear"],
    scheme="FP8_DYNAMIC",
    ignore=["lm_head"],
)

# Apply quantization.
oneshot(model=model, recipe=recipe)

print("========== SAMPLE GENERATION ==============")
dispatch_model(model)
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
print("==========================================")

SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-Dynamic"
model.save_pretrained(SAVE_DIR)
tokenizer.save_pretrained(SAVE_DIR)
  ```
</details>