gemma-4-26B-A4B-it-FP8-Dynamic

Model Overview

• Model Architecture: google/gemma-4-26B-A4B-it
  • Input: Text / Image
  • Output: Text
• Model Optimizations:
  • Weight quantization: FP8
  • Activation quantization: FP8
• Release Date: 2026-04-04
• Version: 1.0
• Model Developers: RedHatAI

This model is a quantized version of google/gemma-4-26B-A4B-it. It was evaluated on several tasks to assess its quality in comparison to the unquantized model.

Model Optimizations

This model was obtained by quantizing the weights and activations of google/gemma-4-26B-A4B-it to FP8 data type using dynamic per-token quantization, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

Weights are quantized statically using per-channel FP8 scaling, and activations are quantized dynamically at inference time using per-token scaling. Only the weights and activations of the linear operators within transformer blocks are quantized using LLM Compressor. Vision tower, embedding, output head, and MoE router layers are kept in their original precision.

Deployment

Use with vLLM

This model can be deployed using vLLM. For detailed instructions including multimodal inference, thinking mode, function calling, and benchmarking, see the Gemma 4 26B-A4B vLLM usage guide.

1. Start the vLLM server:

vllm serve RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic \
 --max-model-len 32768 \
 --gpu-memory-utilization 0.90

To enable thinking/reasoning and tool calling:

vllm serve RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic \
 --max-model-len 32768 \
 --gpu-memory-utilization 0.90 \
 --enable-auto-tool-choice \
 --reasoning-parser gemma4 \
 --tool-call-parser gemma4 \
 --chat-template examples/tool_chat_template_gemma4.jinja \
 --limit-mm-per-prompt '{"image": 4, "audio": 1}' \
 --async-scheduling

Tip: For text-only workloads, pass --limit-mm-per-prompt '{"image": 0, "audio": 0}' to skip vision encoder memory allocation and free up GPU memory for a longer context window.

2. Send requests to the server:

from openai import OpenAI

openai_api_key = "EMPTY"
openai_api_base = "http://<your-server-host>:8000/v1"

client = OpenAI(
 api_key=openai_api_key,
 base_url=openai_api_base,
)

model = "RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic"

messages = [
 {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]

outputs = client.chat.completions.create(
 model=model,
 messages=messages,
)

generated_text = outputs.choices[0].message.content
print(generated_text)

Creation

This model was created by applying data-free FP8 dynamic quantization with LLM Compressor, as presented in the code snippet below.

Evaluation

This model was evaluated on GSM8K Platinum, MMLU-Pro, IFEval, MATH-500, AIME 2025, GPQA Diamond, and LiveCodeBench v6 using lm-evaluation-harness and lighteval, served with vLLM (OpenAI-compatible API). All evaluations were performed with thinking enabled.

Accuracy

Category	Benchmark	google/gemma-4-26B-A4B-it	RedHatAI/gemma-4-26B-A4B-it-FP8-Dynamic	Recovery
Instruction Following	IFEval (0-shot, prompt-level strict)	89.96	89.34	99.3%
	IFEval (0-shot, inst-level strict)	93.21	92.69	99.4%
Reasoning	GSM8K Platinum (0-shot, strict-match)	95.43	95.37	99.9%
	MMLU-Pro (0-shot, custom-extract)	83.47	83.26	99.7%
	MATH-500 (0-shot, pass@1)	84.80	85.93	101.3%
	AIME 2025 (0-shot, pass@1)	80.00	80.00	100.0%
	GPQA Diamond (0-shot, pass@1)	73.20	74.75	102.1%
Coding	LiveCodeBench v6 (0-shot, pass@1)	74.48	73.90	99.2%

Reproduction

The results were obtained using the following commands:

Base model

google/gemma-4-26B-A4B

google/gemma-4-26B-A4B-it

(265)