Qwen 3 8BvsGemma 3 12B
Complete comparison between Qwen 3 8B (8B 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 8B | Gemma 3 12B |
|---|---|---|
| Settings | 8B | 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 8B | Gemma 3 12B |
|---|---|---|
| Q4_K_M (lightweight) | 5 GB | 7 GB |
| Q5_K_M (balanced) | 6 GB | 9 GB |
| Q8 (near-lossless) | 9 GB | 13 GB |
| FP16 (max quality) | 16 GB | 24 GB |
| CPU-only RAM | 10 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 8B | Gemma 3 12B |
|---|---|---|
| RTX 4090 (24 GB) | 90 tok/s · FP16 | 60 tok/s · FP16 |
| RTX 4080 (16 GB) | 35 tok/s · FP16 | 22 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 12 tok/s · Q8 | 7 tok/s · Q5_K_M |
| Apple M4 Pro (48 GB) (36 GB) | 35 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.
Your private, free ChatGPT on your machine in 1 hour — LM Studio, Ollama, Open WebUI, your documents, no cloud.
- Lifetime online access
- PDF + files
- 30-day money-back guarantee
Strengths and weaknesses
Qwen 3 8B
Hybrid thinking/fast mode. 119 languages, 32k native (131k via YaRN).
- Toggleable thinking mode
- 119 languages
- 131k ctx (with YaRN)
- Apache 2.0
- Verbose thinking mode
- Less mature than Qwen 2.5 in terms of ecosystem
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 8B or Gemma 3 12B?
On an RTX 4090, Qwen 3 8B runs at FP16 (~90 tok/s), Gemma 3 12B in FP16 (~60 tok/s). In pure throughput, Qwen 3 8B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 3 8B fits in 5 GB versus 7 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 8B 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: Qwen 3 8B (8B). 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.