Qwen3-8B-FP8-dynamic 👁 Model Icon

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Model Overview

• Model Architecture: Qwen3ForCausalLM
  • Input: Text
  • Output: Text
• Model Optimizations:
  • Activation quantization: FP8
  • Weight quantization: FP8
• Intended Use Cases:
  • Reasoning.
  • Function calling.
  • Subject matter experts via fine-tuning.
  • Multilingual instruction following.
  • Translation.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws).
• Release Date: 05/02/2025
• Version: 1.0
• Validated on: RHOAI 2.24, RHAIIS 3.2.1
• Model Developers: RedHat (Neural Magic)
• ModelCar Storage URI: oci://registry.redhat.io/rhelai1/modelcar-qwen3-8b-fp8-dynamic:1.5
• Validated on RHOAI 2.24: quay.io/modh/vllm:rhoai-2.24-cuda
• Validated on RHAIIS 3.2.1: http://registry.redhat.io/rhaiis/vllm-cuda-rhel9:3.2.1
• Validated on vLLM: 0.10.0

Model Optimizations

This model was obtained by quantizing activations and weights of 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 library is used for quantization.

Deployment

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/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)

messages = [
 {"role": "user", "content": prompt}
]

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)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

Evaluation

The model was evaluated on the OpenLLM leaderboard tasks (versions 1 and 2), using lm-evaluation-harness, and on reasoning tasks using lighteval. vLLM was used for all evaluations.

Accuracy

Category	Benchmark	Qwen3-8B	Qwen3-8B-FP8-dynamic (this model)	Recovery
OpenLLM v1	MMLU (5-shot)	71.95	72.30	100.5%
	ARC Challenge (25-shot)	61.69	61.60	99.9%
	GSM-8K (5-shot, strict-match)	75.97	80.52	106.0%
	Hellaswag (10-shot)	56.52	55.95	99.0%
	Winogrande (5-shot)	65.98	66.22	100.4%
	TruthfulQA (0-shot, mc2)	53.17	52.39	98.5%
	Average	64.21	64.83	101.0%
OpenLLM v2	MMLU-Pro (5-shot)	34.57	37.82	109.4%
	IFEval (0-shot)	84.77	84.56	99.8%
	BBH (3-shot)	25.47	27.20	106.8%
	Math-lvl-5 (4-shot)	51.05	51.90	101.7%
	GPQA (0-shot)	0.00	0.00	---
	MuSR (0-shot)	10.02	10.65	---
	Average	34.31	35.35	103.0%
Multilingual	MGSM (0-shot)	25.97	25.80	99.4%
Reasoning (generation)	AIME 2024	74.58	76.35	102.4%
	AIME 2025	65.21	63.75	97.8%
	GPQA diamond	58.59	61.11	104.3%
	Math-lvl-5	97.60	96.60	99.0%
	LiveCodeBench	56.27	56.60	100.6%

Base model

Qwen/Qwen3-8B-Base

Qwen/Qwen3-8B

(308)

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