Google · 12B · Dense
Multimodal Gemma with 128K context
Use Cases
| Quant | Bits | VRAM | Quality | Status |
|---|---|---|---|---|
| Q2_K | 2 | 4.3 GB | low | — |
| Q3_K_M | 3 | 5.9 GB | moderate | — |
| Q4_K_M | 4 | 6.6 GB | good | — |
| Q5_K_M | 5 | 8.2 GB | good | — |
| Q6_K | 6 | 9.7 GB | excellent | — |
| Q8_0 | 8 | 12.8 GB | excellent | — |
| F16 | 16 | 25.1 GB | lossless | — |
GPUs that run Gemma 3 12B
Gemma 3 12B needs about 6.6 GB of VRAM at Q4_K_M, so it runs comfortably on a GPU with 8 GB of VRAM or more (140 of the GPUs we track). Common cards, smallest first:
| VRAM | GPUs |
|---|---|
| 8 GB | RTX 5060 Ti , RX 7600 , Arc A750 |
| 10 GB | RTX 3080 , RX 6700M |
| 11 GB | RTX 2080 Ti , GTX 1080 Ti |
| 12 GB | RTX 5070 , RX 7700 XT , RTX 4070 Ti |
About this model
This model requires Ollama 0.6 or later. Download Ollama
Gemma is a lightweight, family of models from Google built on Gemini technology. The Gemma 3 models are multimodal—processing text and images—and feature a 128K context window with support for over 140 languages. Available in 270M, 1B, 4B, 12B, and 27B parameter sizes, they excel in tasks like question answering, summarization, and reasoning, while their compact design allows deployment on resource-limited devices.
Models
Text
270M parameter model (32k context window)
ollama run gemma3:270m
1B parameter model (32k context window)
ollama run gemma3:1b
Multimodal (Vision)
4B parameter model (128k context window)
ollama run gemma3:4b
12B parameter model (128k context window)
ollama run gemma3:12b
27B parameter model (128k context window)
ollama run gemma3:27b
Quantization aware trained models (QAT)
The quantization aware trained Gemma 3 models preserves similar quality as half precision models (BF16) while maintaining a lower memory footprint (3x less compared to non-quantized models).
1B parameter model
ollama run gemma3:1b-it-qat
4B parameter model
ollama run gemma3:4b-it-qat
12B parameter model
ollama run gemma3:12b-it-qat
27B parameter model
ollama run gemma3:27b-it-qat
Evaluation
Benchmark Results
Gemma 3 270M
| Benchmark | n-shot | Gemma 3 270m instruction tuned |
|---|---|---|
| HellaSwag | 0-shot | 37.7 |
| PIQA | 0-shot | 66.2 |
| ARC-c | 0-shot | 28.2 |
| WinoGrande | 0-shot | 52.3 |
| BIG-Bench Hard | few-shot | 26.7 |
| IF Eval | 0-shot | 51.2 |
These models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation:
Reasoning, logic and code capabilities
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
|---|---|---|---|---|---|
| HellaSwag | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| BoolQ | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| PIQA | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| SocialIQA | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| TriviaQA | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| Natural Questions | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| ARC-c | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| ARC-e | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| WinoGrande | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| BIG-Bench Hard | 28.4 | 50.9 | 72.6 | 77.7 | |
| DROP | 3-shot, F1 | 42.4 | 60.1 | 72.2 | 77.2 |
| AGIEval | 3-5-shot | 22.2 | 42.1 | 57.4 | 66.2 |
| MMLU | 5-shot, top-1 | 26.5 | 59.6 | 74.5 | 78.6 |
| MATH | 4-shot | – | 24.2 | 43.3 | 50.0 |
| GSM8K | 5-shot, maj@1 | 1.36 | 38.4 | 71.0 | 82.6 |
| GPQA | 9.38 | 15.0 | 25.4 | 24.3 | |
| MMLU (Pro) | 5-shot | 11.2 | 23.7 | 40.8 | 43.9 |
| MBPP | 3-shot | 9.80 | 46.0 | 60.4 | 65.6 |
| HumanEval | pass@1 | 6.10 | 36.0 | 45.7 | 48.8 |
| MMLU (Pro COT) | 5-shot | 9.7 | NaN | NaN | NaN |
Multilingual capabilities
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
|---|---|---|---|---|
| MGSM | 2.04 | 34.7 | 64.3 | 74.3 |
| Global-MMLU-Lite | 24.9 | 57.0 | 69.4 | 75.7 |
| Belebele | 26.6 | 59.4 | 78.0 | – |
| WMT24++ (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| FloRes | 29.5 | 39.2 | 46.0 | 48.8 |
| XL-Sum | 4.82 | 8.55 | 12.2 | 14.9 |
| XQuAD (all) | 43.9 | 68.0 | 74.5 | 76.8 |
Multimodal capabilities
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
|---|---|---|---|
| COCOcap | 102 | 111 | 116 |
| DocVQA (val) | 72.8 | 82.3 | 85.6 |
| InfoVQA (val) | 44.1 | 54.8 | 59.4 |
| MMMU (pt) | 39.2 | 50.3 | 56.1 |
| TextVQA (val) | 58.9 | 66.5 | 68.6 |
| RealWorldQA | 45.5 | 52.2 | 53.9 |
| ReMI | 27.3 | 38.5 | 44.8 |
| AI2D | 63.2 | 75.2 | 79.0 |
| ChartQA | 45.4 | 60.9 | 63.8 |
| ChartQA (augmented) | 81.8 | 88.5 | 88.7 |
| VQAv2 | – | – | – |
| BLINK | 38.0 | 35.9 | 39.6 |
| OKVQA | 51.0 | 58.7 | 60.2 |
| TallyQA | 42.5 | 51.8 | 54.3 |
| SpatialSense VQA | 50.9 | 60.0 | 59.4 |
| CountBenchQA | 26.1 | 17.8 | 68.0 |