Gemma 4 E4B
By Google · United States
Updated 2026-07-13
Overview
Google's 4B-effective multimodal Gemma variant tuned for laptops and edge devices, handling text, image, and audio across 140 languages with a 128K context.
When to pick this model
- Multimodal apps running on laptops or mobile
- Offline assistants that need image and audio input
- Multilingual edge deployments
- Low-power on-device inference
- Prototyping multimodal flows before scaling up
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 10 GB |
| Q5_K_M | 12 GB |
| Q8_0 | 18 GB |
| FP16 (no quantization) | 33 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 4 E4B needs a 12 GB card at Q4_K_M (10 GB). Stepping up to Q8_0 nearly doubles the footprint to 18 GB, and unquantized FP16 weights take 33 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Gemma 4 E4B needs roughly 12 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 14 tokens/sec on entry-level GPUs, on the order of 40 tokens/sec on a mid-range card, and up to 100 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Gemma 4 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 4 E4B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 10 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (12 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q5_K_M (12 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q8_0 (18 GB used) |
| 32 GB | RTX 5090 | Q8_0 (18 GB used) |
Which GPU should you buy to run Gemma 4 E4B?
To run Gemma 4 E4B locally at Q4, you need ~10 GB of VRAM. The best value for this is a RTX 5070 (12 GB VRAM).
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Strengths
- Full text + image + audio in a 4B model
- Runs comfortably on laptops and high-end phones
- 128K context is generous for the size class
- 140-language coverage in a small footprint
Limitations
- Gemma license restricts some commercial uses
- Quality clearly trails 12B+ multimodal models
- Audio reasoning is functional but not robust
Typical workloads
In our catalog grid, Gemma 4 E4B is filed under Edge Multimodal, On-device, Tiny audio — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents; audio understanding; multilingual workloads.
The 125k-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: Dense E4B (4B effective) · multimodal text+image+audio
Training: Edge/mobile edition of Gemma 4.
The most capable sub-5B multimodal model for edge deployments, with the usual Gemma license caveats.
Quick start
ollama run gemma4: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 4 E4B need?
At the recommended Q4_K_M quantization, Gemma 4 E4B needs about 10 GB of VRAM. Q8_0 takes 18 GB, and unquantized FP16 weights take 33 GB.
Can Gemma 4 E4B run without a GPU?
Yes — with roughly 12 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 4 E4B support?
Gemma 4 E4B supports a 125k-token context window (128,000 tokens).
Can I use Gemma 4 E4B commercially?
Gemma 4 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 4 E4B on consumer hardware?
Our compatibility engine estimates on the order of 40 tokens/sec on a mid-range GPU and up to 100 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 4 E4B should I download first?
Start with Q4_K_M (10 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. On a 24 GB card you can run up to Q8_0.