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, 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 using block-wise FP8 scaling (128×128 blocks), and activations are quantized dynamically per group (group_size=128). 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.
- Start the vLLM server:
vllm serve RedHatAI/gemma-4-31B-it-FP8-block \
--tensor-parallel-size 2 \
--max-model-len 32768 \
--gpu-memory-utilization 0.90
To enable thinking/reasoning and tool calling:
vllm serve RedHatAI/gemma-4-31B-it-FP8-block \
--tensor-parallel-size 2 \
--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.
- 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-block"
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 block 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-block"
model_free_ptq(
model_stub=MODEL_ID,
save_directory=SAVE_DIR,
scheme="FP8_BLOCK",
ignore=["re:.*vision.*", "lm_head", "re:.*embed_tokens.*"],
max_workers=8,
device="cuda:0",
)
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
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-block \
--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-block,max_length=69632,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=32,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=3600" \
--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-block,max_length=69632,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=32,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=3600" \
--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-block,max_length=69632,base_url=http://0.0.0.0:8000/v1/chat/completions,num_concurrent=32,max_retries=3,tokenized_requests=False,tokenizer_backend=None,timeout=3600" \
--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-block
base_url: http://0.0.0.0:8000/v1
api_key: ''
timeout: 3600
concurrent_requests: 32
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-block \
--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-block
base_url: http://0.0.0.0:8000/v1
api_key: ''
timeout: 1200
concurrent_requests: 32
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
