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gemma
4B parameters
Commercial OK
Multimodal

Gemma 3 4B

4B Gemma 3 for edge. Multimodal.

License: Gemma Terms of Use·Released Mar 12, 2025·Context: 131,072 tokens
Our verdict
By Fredoline Eruo·Last verified May 6, 2026
7.5/10
Positioning

The 4B Gemma 3 with multimodal capability. Genuinely the best small-model pick when image input matters and VRAM is constrained — fits in under 4 GB at Q4.

Strengths
  • Native multimodal at 4B — no other model in this size class does this credibly.
  • Conversational quality materially better than Phi 3.5 Mini for general chat.
  • 128K context even at this size.
Limitations
  • Gemma license restrictiveness.
  • Math and structured tasks weaker than Phi 3.5 Mini.
  • Knowledge breadth narrow — small-model limitations are real.
Real-world performance on RTX 4090
  • Q4_K_M (2.7 GB): 130–150 tok/s decode, TTFT under 50 ms
  • Q5_K_M (3.2 GB): 115–135 tok/s
  • Q8_0 (4.8 GB): 95–115 tok/s
Should you run this locally?

Yes, for edge devices with multimodal input requirements, 4–6 GB GPU owners who want chat + vision. No, for math/structured tasks (pick Phi 3.5 Mini), or where chat-only ≥ 8B is a better fit.

How it compares
  • vs Phi-3.5 Mini (3.8B) → Gemma 3 4B wins on chat + multimodal; Phi wins on math + structured output.
  • vs Llama 3.2 3B → similar text capability; Gemma adds multimodal.
  • vs Gemma 3 1B → 4B is meaningfully smarter; 1B is for very tight constraints.
Run this yourself 
ollama pull gemma3:4b-it-q4_K_M
ollama run gemma3:4b-it-q4_K_M
Settings: Q4_K_M GGUF, 8192 ctx, llama.cpp/CUDA, RTX 4090
›Why this rating

7.5/10 — best 4B-class general model when you want multimodal at edge size. Loses to Phi-3.5 Mini on math + structured tasks but beats it on chat naturalness.

Overview

4B Gemma 3 for edge. Multimodal.

Strengths

  • Multimodal at 4B
  • Edge-class

Weaknesses

  • License restrictions

Quantization variants

Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.

QuantizationFile sizeVRAM required
Q4_K_M2.5 GB4 GB
Q8_04.4 GB6 GB

Get the model

Ollama

One-line install

ollama run gemma3:4bRead our Ollama review →

HuggingFace

Original weights

huggingface.co/google/gemma-3-4b-it

Source repository — direct quantization required.

Hardware that runs this

Cards with enough VRAM for at least one quantization of Gemma 3 4B.

NVIDIA GB200 NVL72
13824GB · nvidia
AMD Instinct MI355X
288GB · amd
AMD Instinct MI325X
256GB · amd
AMD Instinct MI300X
192GB · amd
NVIDIA B200
192GB · nvidia
NVIDIA H100 NVL
188GB · nvidia
NVIDIA H200
141GB · nvidia
Intel Gaudi 3
128GB · intel
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Frequently asked

What's the minimum VRAM to run Gemma 3 4B?

4GB of VRAM is enough to run Gemma 3 4B at the Q4_K_M quantization (file size 2.5 GB). Higher-quality quantizations need more.

Can I use Gemma 3 4B commercially?

Yes — Gemma 3 4B ships under the Gemma Terms of Use, which permits commercial use. Always read the license text before deployment.

What's the context length of Gemma 3 4B?

Gemma 3 4B supports a context window of 131,072 tokens (about 131K).

How do I install Gemma 3 4B with Ollama?

Run `ollama pull gemma3:4b` to download, then `ollama run gemma3:4b` to start a chat session. The default quantization is Q4_K_M.

Does Gemma 3 4B support images?

Yes — Gemma 3 4B is multimodal and accepts text + vision inputs. Vision support requires a runner that handles its image-conditioning architecture.

Source: huggingface.co/google/gemma-3-4b-it

Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.