Gemma 3 12BvsGemma 2 9B
Complete comparison between Gemma 3 12B (12B parameters, Google) and Gemma 2 9B (9B, Google). VRAM required for each quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, and installation commands. All figures are calculated from the catalog data—no copy-pasting between pages.
In brief
| Feature | Gemma 3 12B | Gemma 2 9B |
|---|---|---|
| Settings | 12B | 9B |
| Family | Gemma | Gemma |
| Author | ||
| Origin | US | US |
| License | Gemma | Gemma |
| Context | 128,000 tokens | 8,192 tokens |
| Output | March 2025 | June 2024 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Gemma 3 12B | Gemma 2 9B |
|---|---|---|
| Q4_K_M (lightweight) | 7 GB | 6 GB |
| Q5_K_M (balanced) | 9 GB | 7.5 GB |
| Q8 (near-lossless) | 13 GB | 11 GB |
| FP16 (max quality) | 24 GB | 20 GB |
| CPU-only RAM | 14 GB | 12 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Gemma 3 12B | Gemma 2 9B |
|---|---|---|
| RTX 4090 (24 GB) | 60 tok/s · FP16 | 75 tok/s · FP16 |
| RTX 4080 (16 GB) | 22 tok/s · Q8 | 28 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 7 tok/s · Q5_K_M | 9 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 22 tok/s · FP16 | 28 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Gemma 3 12B
Multimodal sweet spot. 128k ctx, vision, 140 languages.
- Multimodal sweet spot
- 128k ctx
- 140 languages
- License Gemma
- 9 GB RAM minimum for Ollama
Installation
Gemma 2 9B
Llama 3 quality with a slightly larger footprint.
- Outperforms Llama 3 8B on several benchmarks
- License Gemma
- Good quality-to-size ratio
- 8192 context only
- No vision
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Gemma 3 12B or Gemma 2 9B?
On an RTX 4090, Gemma 3 12B runs at FP16 (~60 tok/s), Gemma 2 9B in FP16 (~75 tok/s). In pure throughput, Gemma 2 9B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 2 9B fits in 6 GB versus 7 GB for its rival — a 1 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Gemma 3 12B is under Gemma — check the restrictions (especially the monthly user thresholds). Gemma 2 9B is under Gemma — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Gemma 2 9B (9B). More capable: Gemma 3 12B (12B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.