Qwen 3 14BvsGemma 3 12B
Complete comparison between Qwen 3 14B (14B parameters, Alibaba) and Gemma 3 12B (12B, Google). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Qwen 3 14B | Gemma 3 12B |
|---|---|---|
| Settings | 14B | 12B |
| Family | Qwen | Gemma |
| Author | Alibaba | |
| Origin | CN | US |
| License | Apache 2.0 | Gemma |
| Context | 131,072 tokens | 128,000 tokens |
| Output | April 2025 | March 2025 |
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 | Qwen 3 14B | Gemma 3 12B |
|---|---|---|
| Q4_K_M (lightweight) | 9 GB | 7 GB |
| Q5_K_M (balanced) | 11 GB | 9 GB |
| Q8 (near-lossless) | 16 GB | 13 GB |
| FP16 (max quality) | 28 GB | 24 GB |
| CPU-only RAM | 16 GB | 14 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 | Qwen 3 14B | Gemma 3 12B |
|---|---|---|
| RTX 4090 (24 GB) | 55 tok/s · Q8 | 60 tok/s · FP16 |
| RTX 4080 (16 GB) | 20 tok/s · Q8 | 22 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 6 tok/s · Q5_K_M | 7 tok/s · Q5_K_M |
| Apple M4 Pro (48 GB) (36 GB) | 20 tok/s · FP16 | 22 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
Qwen 3 14B
Dense 14B with hybrid thinking. Equals Qwen 2.5 32B Based on STEM/code.
- Dense 14B matching Qwen 2.5 32B Base
- Hybrid thinking
- 131k ctx
- Not yet at the level of specialized reasoners on AIME
Installation
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
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 3 14B or Gemma 3 12B?
On an RTX 4090, Qwen 3 14B runs at Q8 (~55 tok/s), Gemma 3 12B in FP16 (~60 tok/s). In pure throughput, Gemma 3 12B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 3 12B fits in 7 GB versus 9 GB for its rival—a 2 GB difference, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Qwen 3 14B is under Apache 2.0 — free for commercial use. Gemma 3 12B 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 3 12B (12B). More capable: Qwen 3 14B (14B). 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.