Gemma 4 Architecture Explained: MoE, Dense, and Why It Matters

You don't need a PhD to understand how Gemma 4 works. But knowing the basics of its architecture will help you pick the right model, understand why it's fast (or slow) on your hardware, and get better results from it.

Let's break it down without the academic jargon.

The Transformer Foundation (30-Second Version)

Every modern language model, Gemma 4 included, is built on the Transformer architecture. Here's all you need to know:

1. Text goes in as tokens (word pieces)
2. Attention layers figure out which tokens relate to each other
3. Feed-forward layers process those relationships
4. Text comes out one token at a time

Gemma 4 stacks dozens of these layers on top of each other. The more layers and the wider they are, the smarter the model — but also the bigger and slower.

Dense vs. MoE: The Two Architectures

Gemma 4 comes in two flavors, and this is the single most important thing to understand about the model lineup.

Dense Models (E2B, E4B)

In a Dense model, every parameter is used for every token. If the model has 4 billion parameters, all 4 billion fire for each word you generate.

Think of it like a small team where everyone works on every task:

• Simple, predictable performance
• All parameters contribute to every response
• Smaller total size, straightforward to run

MoE Models (26B, 31B)

MoE stands for Mixture of Experts. The key insight: you don't need every parameter for every token. Instead, the model has a collection of specialized "experts," and a router decides which ones to activate for each token.

Here's what that looks like in practice:

Input Token → Router → selects 2 of 16 experts → Output

Total parameters:   26 billion
Active per token:   ~3.8 billion (26B model)

It's like a hospital with specialists. When you walk in with a broken arm, you don't need every doctor — you need an orthopedic surgeon and maybe a radiologist. The reception desk (router) sends you to the right experts.

Why the 26B Model Only Uses 3.8B Active Parameters

This is Gemma 4's secret weapon. The 26B MoE model has 26 billion total parameters, but only about 3.8 billion are active for any given token. That means:

You get the knowledge of a 26B model with the speed of a ~4B model. This is why MoE is such a big deal — it breaks the traditional tradeoff between quality and speed.

For a practical comparison of which model to pick, check out our model selection guide.

How the Router Works

The router is a small neural network that sits at the beginning of each MoE layer. For every incoming token, it:

1. Looks at the token's representation
2. Scores each expert (how relevant is this expert for this token?)
3. Picks the top-K experts (usually 2)
4. Combines their outputs using the scores as weights

The router learns during training which experts are good at what. Over time, different experts specialize — some get good at code, others at reasoning, others at creative writing. The router figures out the right mix on the fly.

Load balancing is critical in MoE training. If one expert gets all the tokens (a "collapsed" router), you've wasted the other experts. Gemma 4 uses auxiliary loss functions to keep the load balanced across experts.

The Attention Mechanism

Gemma 4 uses Grouped Query Attention (GQA), which is a middle ground between the original multi-head attention (expensive but high quality) and multi-query attention (cheap but lower quality).

In GQA:

• Query heads are grouped together
• Each group shares one set of key-value heads
• This reduces memory for the KV cache without hurting quality much

Why this matters for you: the KV cache is what grows when you use long contexts. GQA keeps it manageable, which is how Gemma 4 can handle very long inputs without blowing up your VRAM.

256K Context Window

Gemma 4 supports up to 256K tokens of context — roughly 200,000 words or a 400-page book. Here's how it works:

RoPE (Rotary Position Embeddings): Instead of fixed position IDs that max out at a certain length, RoPE encodes positions as rotations. This scales naturally to longer sequences and generalizes better to lengths the model hasn't seen much during training.

Practical context lengths:

Important caveat: Just because the model supports 256K doesn't mean you should always use it. The KV cache grows linearly with context length, and attention computation grows quadratically. For most tasks, 8K-32K is plenty. Save the long context for when you genuinely need it — like analyzing an entire codebase or a full legal contract.

Why Gemma 4 Is Efficient Per-Parameter

Several architectural choices make Gemma 4 punch above its weight:

1. MoE routing: Only 15-20% of parameters active per token
2. GQA: Reduced KV cache memory
3. SwiGLU activation: Better information flow in feed-forward layers
4. RMSNorm: Faster normalization than LayerNorm
5. Optimized tokenizer: 256K vocabulary covers more languages efficiently

The result: the 26B MoE model often matches or beats Dense models with 2-3x more active parameters on standard benchmarks.

Architecture Summary

What This Means for You

• Choosing a model: If you're torn between the 26B MoE and a Dense model of similar total size, the MoE will be faster with comparable quality. See our architecture comparison with Llama 4.
• Estimating VRAM: MoE models need VRAM for all parameters (they're all in memory), but compute scales with active parameters. Check our hardware guide.
• Long context tasks: Start with shorter contexts and only expand when needed. Your VRAM will thank you.
• Fine-tuning: MoE models can be fine-tuned with LoRA, targeting the attention layers and/or the expert layers.

Next Steps

• Pick the right model with our model selection guide
• Check hardware requirements for your chosen architecture
• See how the architecture performs on Mac Apple Silicon
• Compare architectures: Gemma 4 vs Llama 4