Gemma 4 E2B
By Google · United States
Updated 2026-07-13
Overview
Google's edge-optimized Gemma 4: 2B effective params, full text + image multimodal, 128k context, and a configurable thinking mode. Built for laptops, mobile, and CPU inference.
When to pick this model
- On-device multimodal apps on laptops and phones
- CPU or low-end GPU inference at ~7 GB Q4
- Long-context tasks up to 128k at edge scale
- Quick-toggle thinking mode for harder prompts
- 140+ language coverage in a tiny footprint
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 7 GB |
| Q5_K_M | 9 GB |
| Q8_0 | 13 GB |
| FP16 (no quantization) | 25 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 E2B fits an 8 GB consumer card at Q4_K_M (7 GB). Stepping up to Q8_0 nearly doubles the footprint to 13 GB, and unquantized FP16 weights take 25 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Gemma 4 E2B needs roughly 9 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 20 tokens/sec on entry-level GPUs, on the order of 55 tokens/sec on a mid-range card, and up to 130 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 E2B 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 E2B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q4_K_M (7 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (9 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (13 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Q8_0 (13 GB used) |
| 32 GB | RTX 5090 | FP16 (25 GB used) |
Which GPU should you buy to run Gemma 4 E2B?
To run Gemma 4 E2B locally at Q4, you need ~7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Strengths
- Full multimodal in ~7 GB at Q4
- Runs on CPU or entry-level GPU
- 128k context
- Thinking mode toggle
- Open Gemma license
Limitations
- Quality trails the E4B and 26B variants
- Reasoning benchmarks well below larger models
- Gemma license isn't Apache or MIT
Typical workloads
In our catalog grid, Gemma 4 E2B is filed under Edge Mobile, Compact Vision, Low-VRAM Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; vision-language work — screenshots, charts, scanned documents; 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 E2B (2B effective) · multimodal text+image · 128k ctx · configurable thinking
Training: Ultra-compact edge edition of Gemma 4. Architecture optimized for on-device/mobile. 140+ languages.
The Gemma 4 to pick when you're shipping on-device — small, multimodal, and surprisingly long-context.
Quick start
ollama run gemma4:e2bOr 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 E2B need?
At the recommended Q4_K_M quantization, Gemma 4 E2B needs about 7 GB of VRAM. Q8_0 takes 13 GB, and unquantized FP16 weights take 25 GB.
Can Gemma 4 E2B run without a GPU?
Yes — with roughly 9 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 E2B support?
Gemma 4 E2B supports a 125k-token context window (128,000 tokens).
Can I use Gemma 4 E2B commercially?
Gemma 4 E2B 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 E2B on consumer hardware?
Our compatibility engine estimates on the order of 55 tokens/sec on a mid-range GPU and up to 130 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Gemma 4 E2B should I download first?
Start with Q4_K_M (7 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 Q4_K_M.