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Gemma 3 4B Instruct

Gemma 3 4B Instruct needs roughly 3.3 GB VRAM at Q4_K_M quantization (9.5 GB at FP16). 103 GPUs we track can run it fully in VRAM at 8k context.

103 GPUs run this natively · 1 with CPU offload

Google4B params128k contextGemmaCommercial use ok

Gemma 3 4B Instruct is a 4B parameter dense model developed by Google. Compact multimodal model — vision and text in 4B package.

To run Gemma 3 4B Instruct locally: Q6_K ~4GB — runs on 8GB GPUs with vision support.

Multimodal capabilities at edge-friendly size.

VRAM at each quantization

Gemma 3 4B Instruct natively supports a longer context window, but the table below is capped at 8k for comparability — KV cache grows linearly with context length.

QuantWeightsKV cacheTotal
FP3216.0 GB0.50 GB18.5 GB
BF168.0 GB0.50 GB9.5 GB
FP168.0 GB0.50 GB9.5 GB
Q8_04.3 GB0.50 GB5.3 GB
Q6_Krec3.3 GB0.50 GB4.2 GB
Q5_K_M2.9 GB0.50 GB3.8 GB
Q4_K_M2.4 GB0.50 GB3.3 GB
Q3_K_M1.9 GB0.50 GB2.7 GB
Q2_K1.5 GB0.50 GB2.3 GB
NVFP4cuda2.0 GB0.50 GB2.8 GB

Shown at 8k context with FP16 KV cache. NVFP4 needs a CUDA GPU to run. Toggle TurboQuant in the calculator to view compressed KV cache numbers.

Benchmarks

MMLU-Pro
43.6

GPUs that run Gemma 3 4B Instruct natively (103)

Plus 1 GPUs that run it with CPU offload (slower)
Hugging Face ↗Ollama ↗Released 2025-03-12

Compare Gemma 3 4B Instruct with other models

How to run Gemma 3 4B Instruct locally

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Q6_K needs 4.2 GB — fits a single high-end consumer GPU (24 GB).

Ollama

ollama run gemma3:4b

llama.cpp

./llama-cli -m gemma-3-4b-it.Q6_K.gguf -c 8192 -ngl 99

LM Studio: Search for 'Gemma 3 4B' in LM Studio. Small enough to run on virtually any GPU with 4+ GB of VRAM. Supports images too.

Why this quantization? At just 4B parameters, Q6_K only requires about 3.5 GB of VRAM for weights, leaving plenty of headroom on even budget GPUs. The compact size means the quality difference between Q4 and Q6 is noticeable, so spending the extra ~500 MB on Q6 is worthwhile. This is one of the few multimodal models small enough to run with generous quantization on low-end hardware.

Who is Gemma 3 4B Instruct for?

Anyone with a budget GPU (4-8 GB VRAM), a laptop, or an edge device who still wants multimodal capability. Perfect for students and hobbyists exploring vision-language models for the first time without needing expensive hardware.

Best for

  • Learning and experimenting with multimodal AI on budget hardware
  • Simple image captioning and visual question answering
  • Lightweight text generation and basic chat
  • On-device and embedded applications with tight VRAM constraints

Not ideal for

  • Tasks requiring high accuracy on complex queries -- this is an entry-level model
  • Professional content generation or coding assistance
  • Advanced reasoning or math (MMLU-Pro: 43.6 is modest)

Continue reading

vram-guides8 min
Best LLMs You Can Run on 8 GB VRAM (2026)
vram-guides8 min
Best LLMs for 6 GB VRAM (2026)
tutorials11 min
Getting Started with Local LLMs (2026): The Complete Beginner's Guide

Frequently asked questions

What are the VRAM requirements for Gemma 3 4B Instruct?
Gemma 3 4B Instruct requires approximately 3.3 GB of VRAM at Q4_K_M quantization, 5.3 GB at Q8, and 9.5 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does Gemma 3 4B Instruct have?
Gemma 3 4B Instruct has 4 billion parameters.
How capable is Gemma 3 4B Instruct?
Gemma 3 4B Instruct has an MMLU-Pro score of 43.6, making it well-suited for lightweight tasks, prototyping, and resource-constrained environments.
Can Gemma 3 4B Instruct run on a 16 GB GPU?
Yes. Gemma 3 4B Instruct needs 3.3 GB at Q4_K_M, which fits in a 16 GB GPU like the RTX 4080 or RTX 4070 Ti Super.
What is the smallest quantization for Gemma 3 4B Instruct that fits in 24 GB of VRAM?
At FP32, Gemma 3 4B Instruct needs 18.5 GB — the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Gemma 3 4B Instruct locally?
A 16 GB GPU is enough. At Q4_K_M, Gemma 3 4B Instruct needs 3.3 GB VRAM. Good options: RTX 4080 (16 GB), RTX 4070 Ti Super (16 GB).