Gemma 3 4BvsGemma 2 2B
Complete comparison between Gemma 3 4B (4B parameters, Google) and Gemma 2 2B (2B, 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 | Gemma 3 4B | Gemma 2 2B |
|---|---|---|
| Settings | 4B | 2B |
| 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 4B | Gemma 2 2B |
|---|---|---|
| Q4_K_M (lightweight) | 3.5 GB | 1.8 GB |
| Q5_K_M (balanced) | 4 GB | 2.2 GB |
| Q8 (near-lossless) | 5 GB | 3.2 GB |
| FP16 (max quality) | 9 GB | 5 GB |
| CPU-only RAM | 6 GB | 4 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 4B | Gemma 2 2B |
|---|---|---|
| RTX 4090 (24 GB) | 150 tok/s · FP16 | 200 tok/s · FP16 |
| RTX 4080 (16 GB) | 65 tok/s · FP16 | 100 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 22 tok/s · FP16 | 35 tok/s · FP16 |
| Apple M4 Pro (48 GB) (36 GB) | 65 tok/s · FP16 | 100 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 4B
Compact multimodal model. 140+ languages, 128k ctx, vision, sliding-window attention.
- Compact multimodal
- 140 languages
- 128k ctx
- Gemma license (not Apache)
Installation
Gemma 2 2B
Tiny and efficient. Runs on Raspberry Pi 5.
- Very lightweight (1.8 GB VRAM Q4)
- Best 2B of 2024
- Runs on a modest CPU
- Open Gemma license
- 8192 context only
- Limited on complex tasks
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Gemma 3 4B or Gemma 2 2B?
On an RTX 4090, Gemma 3 4B runs in FP16 (~150 tok/s), Gemma 2 2B in FP16 (~200 tok/s). In pure throughput, Gemma 2 2B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Gemma 2 2B fits in 1.8 GB versus 3.5 GB for its rival—a 1.7 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 3 4B is under Gemma — check the restrictions (especially the monthly user thresholds). Gemma 2 2B 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 2B (2B). More capable: Gemma 3 4B (4B). 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.