gemma-3-1b-it-quantized.w8a8
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gemma-3-27b-it-FP8-dynamic
RedHatAI- **Model Architecture:** gemma-3-27b-it - **Input:** Vision-Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** 2/24/2025 - **Version:** 1.0 - **Model Developers:** Neural Magic Quantized version of google/gemma-3-27b-it. This model was obtained by quantizing the weights of google/gemma-3-27b-it to FP8 data type, ready for inference with vLLM >= 0.5.2.
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View details ↗gemma-3-27b-it-quantized.w4a16
RedHatAI--- tags: - vllm - vision - w4a16 license: gemma base_model: google/gemma-3-27b-it library_name: transformers --- - **Model Architecture:** google/gemma-3-27b-it - **Input:** Vision-Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Activation quantization:** FP16 - **Release Date:** 6/4/2025 - **Version:** 1.0 - **Model Developers:** RedHatAI Quantized version of google/gemma-3-27b-it.
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View details ↗gemma-3-4b-it-FP8-dynamic
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View details ↗gemma-3-4b-it-quantized.w4a16
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View details ↗gemma-3-4b-it-quantized.w8a8
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View details ↗gemma-4-12B-it
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View details ↗gemma-4-12B-it-FP8-Dynamic
RedHatAIThis is a preliminary version (and subject to change) of FP8_Dynamic quantized google/gemma-4-12B-it model. The model has both weights and activations quantized to FP8_Dynamic format with vllm-project/llm-compressor. Run this script with this LLM Compressor PR and latest transformers to quantize the model using iMatrix quantization Evals: | model_name | flexible-extract | strict-match | bits_per_byte | byte_ppl | |---------------|------------------|--------------|---------------|----------| | baseline-bf16 | 0.9082 | 0.8958 | 1.9125 | 3.7645 | | FP8-RTN | 0.9115 | 0.8999 | 1.9368 | 3.8285 | | *FP8-iMatrix* | 0.9198 | 0.9032 | 1.9056 | 3.7465 | | FP8-GPTQ | 0.9098 | 0.8950 | 1.9357 | 3.8257 |
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View details ↗gemma-4-12B-it-NVFP4
RedHatAIThis is a preliminary version (and subject to change) of NVFP4 quantized google/gemma-4-12B-it model. The model has both weights and activations quantized to NVFP4 format with vllm-project/llm-compressor. Run this script with this LLM Compressor PR and latest transformers to quantize the model using GPTQ
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View details ↗gemma-4-26B-A4B-it
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View details ↗gemma-4-26B-A4B-it-FP8-dynamic
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **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. 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%.
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View details ↗gemma-4-26B-A4B-it-NVFP4
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **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. This model was obtained by quantizing the weights and activations of google/gemma-4-26B-A4B-it to NVFP4 data type, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
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View details ↗gemma-4-31B-it
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View details ↗gemma-4-31B-it-FP8-block
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **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. 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%.
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View details ↗gemma-4-31B-it-FP8-dynamic
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **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. 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%.
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View details ↗gemma-4-31B-it-NVFP4
RedHatAI- **Model Architecture:** Gemma4ForConditionalGeneration - **Input:** Text / Image - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **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. This model was obtained by quantizing the weights and activations of google/gemma-4-31B-it to FP4 data type using the NVFP4 format, ready for inference with vLLM. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
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View details ↗gemma-4-31B-it-speculator.dflash
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View details ↗gemma-4-31B-it-speculator.dspark
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View details ↗gemma-4-31B-it-speculator.eagle3
RedHatAIThis is a preliminary model release, we will continue to train the model and improve the acceptance rates in the next few days. This is a speculator model designed for use with google/gemma-4-31b-it, based on the EAGLE-3 speculative decoding algorithm. It was trained using the Speculators library on a combination of the Magpie-Align/Magpie-Llama-3.1-Pro-300K-Filtered dataset and the `train_sft` split of the HuggingFaceH4/ultrachat_200k dataset. Training data used **Magpie + UltraChat with responses from the gemma-4-31B-it model (no reasoning)**. This model should be used with the google/gemma-4-31b-it chat template, specifically through the `/chat/completions` endpoint.
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View details ↗gemma-4-E4B-it
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View details ↗GLM-5.2-FP8
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View details ↗GLM-5.2-NVFP4-FP8
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