Which Gemma 4 Model Should I Use? (E2B vs E4B vs 26B vs 31B)

Gemma 4 comes in four flavors, and picking the right one makes a huge difference. Run one that's too big and you'll be staring at a loading spinner. Run one that's too small and the quality won't be there. Let's figure out which one is right for you.

The Four Models at a Glance

The key thing to notice: the 26B model is a Mixture of Experts (MoE). It has 26 billion total parameters, but only activates about 3.8 billion at a time. That means it's way more efficient than the number suggests — you get big-model quality at small-model speed. For a deeper dive into the MoE architecture, see our 26B vs 31B comparison.

Model-by-Model Breakdown

E2B — The Pocket Rocket

2 billion parameters, ~4 GB RAM

This is the smallest Gemma 4 model, built for situations where resources are tight. Think mobile phones, Raspberry Pi, embedded devices, or when you need super fast responses and don't need deep reasoning.

ollama run gemma4:e2b

Good at:

• Quick text generation and summarization
• Simple Q&A
• Classification tasks
• Running on phones and edge devices
• Situations where latency matters more than depth

Limitations:

• Struggles with complex multi-step reasoning
• Less nuanced creative writing
• Can miss context in longer conversations

E4B — The Sweet Spot (Recommended)

4 billion parameters, ~6 GB RAM

If you're reading this and don't know which to pick, this is probably the one. E4B runs comfortably on any modern laptop — Mac, Windows, Linux — and delivers surprisingly good quality for its size.

ollama run gemma4:e4b

Good at:

• General-purpose chat and Q&A
• Code generation and explanation
• Content writing and editing
• Multimodal tasks (images + text)
• Daily driver for local AI

Why it's the default recommendation:

• Runs on basically any laptop made in the last 3-4 years
• Fast enough for interactive chat (easily 20+ tokens/sec on Apple Silicon)
• Quality is genuinely good — it punches way above its weight class
• Low enough resource usage to run alongside your other apps

26B A4B — The Efficiency King

26B total, only 3.8B active (MoE architecture), ~8-18 GB RAM

This model is the most interesting one in the lineup. It uses Mixture of Experts — Google trained 26 billion parameters, but for any given input, only about 3.8B activate. You get the knowledge of a large model with the speed of a small one.

ollama run gemma4:26b

Good at:

• Complex reasoning and analysis
• Coding tasks across multiple languages
• Long-form content generation
• Specialized knowledge questions
• Best quality-per-FLOP in the lineup

The catch:

• While active parameters are small, you still need to fit all 26B in memory
• With GGUF Q4 quantization, expect around 8-16 GB depending on context length
• MoE models can have slightly more variable output quality (different experts activate for different inputs)

Who should use this: If you have a machine with 16+ GB RAM and a decent GPU (or Apple Silicon Mac), this is arguably the best model in the whole lineup. You get near-31B quality at E4B speed.

31B Dense — Maximum Power

31 billion parameters, all dense, ~20 GB RAM minimum

This is the biggest, most capable Gemma 4 model. Every token processed touches all 31 billion parameters. No shortcuts, no routing — just raw capability.

ollama run gemma4:31b

Good at:

• The most challenging reasoning tasks
• Highest quality creative writing
• Complex code generation and debugging
• Research and analysis
• When quality is the only thing that matters

Requirements:

• Minimum 20 GB RAM (24-32 GB recommended)
• Dedicated GPU strongly recommended for acceptable speed
• At Q4 quantization, the model file itself is around 18 GB

VRAM Requirements (GPU Users)

If you're running on a GPU, here's what you need. For a full breakdown by specific machine (MacBook, gaming PC, cloud), see our hardware requirements guide.

Pro tip: Q4_K_M quantization is the sweet spot for most people. You lose very little quality compared to full precision, and the memory savings are massive.

Watch Out for KV Cache

Here's something that trips people up: the model weights aren't the only thing eating your memory. The KV cache — which stores context from your conversation — can get huge, especially with Gemma 4's massive context window.

Community reports on the 31B model show that with a 262K context window, KV cache alone can eat ~22 GB of additional memory. That's on top of the model weights.

Practical advice:

• If you're running into memory issues, try reducing the context length:

  # In Ollama, set a smaller context window
  ollama run gemma4:31b --ctx-size 8192

• For the 26B and 31B models, consider enabling KV cache quantization (Q8 or Q4) to cut memory usage significantly
• The E2B and E4B models are much more reasonable — their KV cache stays manageable even at longer contexts

Decision Tree: What Hardware Do You Have?

"I have a phone or Raspberry Pi" → E2B. It's the only one that'll fit.

"I have a laptop with 8 GB RAM" → E4B. It'll run well and leave room for your other apps.

"I have a laptop/desktop with 16 GB RAM" → E4B for speed, or 26B (quantized) if you want better quality and can wait a bit longer.

"I have 24+ GB RAM or a GPU with 8+ GB VRAM" → 26B is the sweet spot. Seriously, it's incredibly good for the compute cost.

"I have a workstation with 24+ GB VRAM" → 31B Dense for maximum quality. You've got the horsepower, use it.

"I want to use it on my server/cloud" → 26B or 31B, depending on your budget and latency requirements.

Benchmark Comparison

Here's how the models stack up across common benchmarks:

The 26B MoE model is the standout here — it gets close to 31B quality scores while running nearly 3x faster. That MoE architecture really pays off.

Quantization: Which One?

If you're downloading GGUF files from Hugging Face, you'll see options like Q4_K_M, Q5_K_M, Q8_0, etc. Here's what they mean:

My recommendation: Start with Q4_K_M. If you notice quality issues in your specific use case, bump up to Q5_K_M. Most people genuinely cannot tell the difference.

For help getting the model downloaded, head to our complete download guide.

Next Steps

• Ready to download? → Gemma 4 Download Guide (Every Method)
• Check your hardware → Gemma 4 Hardware Requirements
• Running into problems? → Gemma 4 Troubleshooting
• Want to compare with other models? → Gemma 4 vs Llama 4 or Gemma 4 vs Qwen 3