Gemma 4 2BvsGemma 3 27B
Complete comparison between Gemma 4 2B (2B parameters, Google) and Gemma 3 27B (27B, Google). VRAM required by 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 4 2B | Gemma 3 27B |
|---|---|---|
| Settings | 2B | 27B |
| Family | Gemma | Gemma |
| Author | ||
| Origin | US | US |
| License | Gemma | Gemma |
| Context | 128,000 tokens | 128,000 tokens |
| Output | May 6, 2026 | 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 | Gemma 4 2B | Gemma 3 27B |
|---|---|---|
| Q4_K_M (lightweight) | 1.2 GB | 16 GB |
| Q5_K_M (balanced) | 1.4 GB | 19 GB |
| Q8 (near-lossless) | 2.1 GB | 29 GB |
| FP16 (max quality) | 4 GB | 54 GB |
| CPU-only RAM | 2.6 GB | 28 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 4 2B | Gemma 3 27B |
|---|---|---|
| RTX 4090 (24 GB) | 200 tok/s · FP16 | 32 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | 100 tok/s · FP16 | 13 tok/s · Q4_K_M |
| RTX 3060 12GB (12 GB) | 35 tok/s · FP16 | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 100 tok/s · FP16 | 13 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 4 2B
Gemma 4 base 2B multimodal (text+image), Gemma license, 128k ctx. Runs on an integrated GPU or Raspberry Pi 5. Released May 2026.
- Fits on integrated GPU (1.2 GB Q4 VRAM)
- Text + image multimodal
- License Gemma
- 128k context on 2B
- Limited reasoning vs. 4B/26B
- Gated model on Hugging Face (click-through)
Installation
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
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Gemma 4 2B or Gemma 3 27B?
On an RTX 4090, Gemma 4 2B runs at FP16 (~200 tok/s), Gemma 3 27B in Q5_K_M (~32 tok/s). In pure throughput terms, Gemma 4 2B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 4 2B fits in 1.2 GB versus 16 GB for its rival — a 14.8 GB gap, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Can these models be used in commercial production?
Gemma 4 2B is under Gemma — check the restrictions (especially the monthly user thresholds). Gemma 3 27B 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 4 2B (2B). More capable: Gemma 3 27B (27B). If you're unsure, run the 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.