gemma-4-31B-it-FP8-Dynamic

Model Overview

• Model Architecture: google/gemma-4-31B-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-4-31B-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-4-31B-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, and output head layers are kept in their original precision.

Deployment

Use with vLLM

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

1. Start the vLLM server:

vllm serve RedHatAI/gemma-4-31B-it-FP8-Dynamic \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.90

To enable thinking/reasoning and tool calling:

vllm serve RedHatAI/gemma-4-31B-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-4-31B-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.

from llmcompressor import model_free_ptq

MODEL_ID = "google/gemma-4-31B-it"
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"

model_free_ptq(
    model_stub=MODEL_ID,
    save_directory=SAVE_DIR,
    scheme="FP8_DYNAMIC",
    ignore=["re:.*vision.*", "lm_head", "re:.*embed_tokens.*"],
)

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-4-31B-it	RedHatAI/gemma-4-31B-it-FP8-Dynamic	Recovery
Instruction Following	IFEval (0-shot, prompt-level strict)	90.70	91.07	100.4%
	IFEval (0-shot, inst-level strict)	93.45	93.76	100.3%
Reasoning	GSM8K Platinum (0-shot, strict-match)	95.78	95.83	100.1%
	MMLU-Pro (0-shot, custom-extract)	85.41	85.32	99.9%
	MATH-500 (0-shot, pass@1)	89.40	90.27	101.0%
	AIME 2025 (0-shot, pass@1)	65.83	66.25	100.6%
	GPQA Diamond (0-shot, pass@1)	77.44	78.11	100.9%
Coding	LiveCodeBench v6 (0-shot, pass@1)	71.43	70.67	98.9%

Reproduction

The results were obtained using the following commands:

Each benchmark was run 3 times with different random seeds (1234, 2345, 3456) and the scores were averaged; AIME 2025 used 8 seeds.

vLLM server (instruction following and reasoning benchmarks):

vllm serve RedHatAI/gemma-4-31B-it-FP8-Dynamic \
  --tensor-parallel-size 2 \
  --max-model-len 69632 \
  --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":0,"audio":0}' \
  --async-scheduling

GSM8K Platinum (lm-eval, 0-shot, 3 repetitions)

lm_eval --model local-chat-completions \
  --tasks gsm8k_platinum_cot_llama \
  --model_args "model=RedHatAI/gemma-4-31B-it-FP8-Dynamic,max_length=36096,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
  --num_fewshot 0 \
  --apply_chat_template \
  --output_path results_gsm8k_platinum.json \
  --seed 1234 \
  --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,top_k=64,max_gen_toks=32000,seed=1234"

MMLU-Pro (lm-eval, 0-shot, 3 repetitions)

lm_eval --model local-chat-completions \
  --tasks mmlu_pro_chat \
  --model_args "model=RedHatAI/gemma-4-31B-it-FP8-Dynamic,max_length=36096,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
  --num_fewshot 0 \
  --apply_chat_template \
  --output_path results_mmlu_pro.json \
  --seed 1234 \
  --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,top_k=64,max_gen_toks=32000,seed=1234"

IFEval (lm-eval, 0-shot, 3 repetitions)

lm_eval --model local-chat-completions \
  --tasks ifeval \
  --model_args "model=RedHatAI/gemma-4-31B-it-FP8-Dynamic,max_length=36096,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=128,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=1200" \
  --num_fewshot 0 \
  --apply_chat_template \
  --output_path results_ifeval.json \
  --seed 1234 \
  --gen_kwargs "do_sample=True,temperature=1.0,top_p=0.95,top_k=64,max_gen_toks=32000,seed=1234"

MATH-500, AIME 2025, GPQA Diamond (lighteval, 3 repetitions; 8 for AIME 2025)

litellm_config.yaml:

model_parameters:
  provider: hosted_vllm
  model_name: hosted_vllm/RedHatAI/gemma-4-31B-it-FP8-Dynamic
  base_url: http://0.0.0.0:8000/v1
  api_key: ''
  timeout: 3600
  concurrent_requests: 128
  generation_parameters:
    temperature: 1.0
    max_new_tokens: 65536
    top_p: 0.95
    top_k: 64
    seed: 1234

Run once per seed (changing seed in the config each time):

lighteval endpoint litellm litellm_config.yaml 'math_500|0' \
  --output-dir results/ --save-details

lighteval endpoint litellm litellm_config.yaml 'aime25|0' \
  --output-dir results/ --save-details

lighteval endpoint litellm litellm_config.yaml 'gpqa:diamond|0' \
  --output-dir results/ --save-details

LiveCodeBench v6 (lighteval, 3 repetitions)

vLLM server:

vllm serve RedHatAI/gemma-4-31B-it-FP8-Dynamic \
  --tensor-parallel-size 2 \
  --max-model-len 36864 \
  --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":0,"audio":0}' \
  --async-scheduling

litellm_config.yaml:

model_parameters:
  provider: hosted_vllm
  model_name: hosted_vllm/RedHatAI/gemma-4-31B-it-FP8-Dynamic
  base_url: http://0.0.0.0:8000/v1
  api_key: ''
  timeout: 1200
  concurrent_requests: 256
  generation_parameters:
    temperature: 1.0
    max_new_tokens: 32768
    top_p: 0.95
    top_k: 64
    seed: 1234

Run once per seed:

lighteval endpoint litellm litellm_config.yaml 'lcb:codegeneration_v6|0' \
  --output-dir results/ --save-details