Gemma 3n E4B
By Google · United States
Updated 2026-07-13
Overview
Google's full Gemma 3n with 4B effective parameters (8B raw) and nested MatFormer architecture. Native multimodal across 140 languages for high-end mobile deployments.
When to pick this model
- High-end mobile or edge devices needing multimodal input
- Multilingual on-device assistants across 140 languages
- Image-aware mobile workflows
- Replacing E2B when accuracy matters more than RAM
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 4.5 GB |
| Q5_K_M | 5.5 GB |
| Q8_0 | 8 GB |
| FP16 (no quantization) | 14 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Gemma 3n E4B fits an 8 GB consumer card at Q4_K_M (4.5 GB). Stepping up to Q8_0 nearly doubles the footprint to 8 GB, and unquantized FP16 weights take 14 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Gemma 3n E4B needs roughly 8 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 22 tokens/sec on entry-level GPUs, on the order of 65 tokens/sec on a mid-range card, and up to 150 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Gemma 3n E4B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Gemma 3n E4B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q8_0 (8 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q8_0 (8 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | FP16 (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (14 GB used) |
| 32 GB | RTX 5090 | FP16 (14 GB used) |
Which GPU should you buy to run Gemma 3n E4B?
To run Gemma 3n E4B locally at Q4, you need ~4.5 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- 4B effective parameters punch well above mobile-class weights
- Integrated multimodal — text and image input
- 140 language coverage
- Open Gemma license
Limitations
- 32k context only
- Beaten by Gemma 3 12B in desktop scenarios
- Gemma license — less permissive than Apache 2.0
- Multimodal support uneven across runtimes
Typical workloads
In our catalog grid, Gemma 3n E4B is filed under Powerful Mobile, Edge Multilingual — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads.
The 32k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. It ships under the Gemma license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Gemma 3n E4B · on-device architecture · 4B effective
Training: Google Gemma 3n 4B, multimodal text+image, 140 languages.
The full-fat Gemma 3n — strong mobile multimodal with surprising quality, if Gemma's license fits your use case.
Quick start
ollama run gemma3n:e4bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Gemma 3n E4B need?
At the recommended Q4_K_M quantization, Gemma 3n E4B needs about 4.5 GB of VRAM. Q8_0 takes 8 GB, and unquantized FP16 weights take 14 GB.
Can Gemma 3n E4B run without a GPU?
Yes — with roughly 8 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Gemma 3n E4B support?
Gemma 3n E4B supports a 32k-token context window (32,768 tokens).
Can I use Gemma 3n E4B commercially?
Gemma 3n E4B ships under the Gemma license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Gemma 3n E4B on consumer hardware?
Our compatibility engine estimates on the order of 65 tokens/sec on a mid-range GPU and up to 150 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 3n E4B should I download first?
Start with Q4_K_M (4.5 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q8_0.