Gemma 3 27BvsQwen 3 32B
Complete comparison between Gemma 3 27B (27B parameters, Google) and Qwen 3 32B (32B, Alibaba). 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 | Gemma 3 27B | Qwen 3 32B |
|---|---|---|
| Settings | 27B | 32B |
| Family | Gemma | Qwen |
| Author | Alibaba | |
| Origin | US | CN |
| License | Gemma | Apache 2.0 |
| Context | 128,000 tokens | 131,072 tokens |
| Output | March 2025 | April 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 | Gemma 3 27B | Qwen 3 32B |
|---|---|---|
| Q4_K_M (lightweight) | 16 GB | 19 GB |
| Q5_K_M (balanced) | 19 GB | 23 GB |
| Q8 (near-lossless) | 29 GB | 35 GB |
| FP16 (max quality) | 54 GB | 64 GB |
| CPU-only RAM | 28 GB | 32 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 27B | Qwen 3 32B |
|---|---|---|
| RTX 4090 (24 GB) | 32 tok/s · Q5_K_M | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | 13 tok/s · Q4_K_M | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 13 tok/s · Q8 | 12 tok/s · Q8 |
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 27B
High-end Gemma. LMArena Elo 1338 — beats Llama 3.1 405B at 15× smaller size.
- LMArena Elo 1338: beats Llama 3.1 405B while being 15× smaller
- Multimodal
- 128k ctx
- License Gemma
- No thinking mode
Installation
Qwen 3 32B
Dense 32B with thinking mode. MMLU-Pro 65.5, SuperGPQA 39.8.
- Solid reasoning in thinking mode
- 131k ctx
- Apache 2.0
- Worse than QwQ-32B dedicated to pure reasoning
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Gemma 3 27B or Qwen 3 32B?
On an RTX 4090, Gemma 3 27B runs at Q5_K_M (~32 tok/s), Qwen 3 32B in Q5_K_M (~30 tok/s). In pure throughput, Gemma 3 27B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 3 27B fits in 16 GB versus 19 GB for its rival—a 3 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Gemma 3 27B is under Gemma — check the restrictions (especially the monthly user thresholds). Qwen 3 32B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Gemma 3 27B (27B). More capable: Qwen 3 32B (32B). 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.