Gemma 3n E4B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated April 1, 2025

Model library → Gemma 3n → Gemma 3n E4B

The full-power Gemma 3n variant. Processes text, images, audio, and video. Runs on devices with 4 GB RAM, including mid-range phones and all laptops.

Gemma 3n E4B needs about 6 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters4 Billion effective
Context window32,768
ArchitectureMatFormer, multimodal
ProviderGoogle DeepMind
LicenceGemma ToS
Specified atQ4_K_M
System RAM8 GB
Record updated2025-04-01

Licence

Gemma Terms — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), with no KV cache (this record has no published architecture). Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K2.6 GB3.4 GB~213 tok/s (est.)Fits comfortably
Q3_K_M3.3 GB4.1 GB~193 tok/s (est.)Fits comfortably
Q4_K_M4.7 GB5.5 GB~164 tok/s (est.)Fits comfortably
Q5_K_M5.6 GB6.4 GB~151 tok/s (est.)Fits comfortably
Q6_K6.4 GB7.2 GB~139 tok/s (est.)Fits comfortably
Q8_08.3 GB9.1 GB~118 tok/s (est.)Fits comfortably
F1615.7 GB16.5 GB~75 tok/s (est.)Fits comfortably

Want the memory numbers alone, at every quantization level and your own context length? Use the Gemma 3n E4B VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Gemma 3n E4B is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
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How to Run Gemma 3n E4B

Install Ollama, then run:

ollama run gemma3n:e4b

Weights on Hugging Face: google/gemma-3n-E4B-it.

Best for: phone, vision, audio, edge devices, chat.

Can I Run Gemma 3n E4B on My GPU?

Other Gemma 3n Sizes

Gemma 3n E4B — Frequently Asked Questions

How much VRAM does Gemma 3n E4B need?
About 6 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Gemma 3n E4B run on an RTX 4090 (24 GB)?
Yes. Gemma 3n E4B needs about 6 GB at Q4_K_M, inside a 24 GB card, at an estimated 164 tokens/sec.
How do I run Gemma 3n E4B locally?
Install Ollama and run `ollama run gemma3n:e4b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Gemma 3n come in?
Gemma 3n E2B (4 GB), Gemma 3n E4B (6 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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