This is a submission for the Gemma 4 Challenge: Write About Gemma 4

Gemma 4 Explained: Which Model Should You Use and Why It Changes Everything for Local AI

I built an offline AI crop diagnostic tool for Indian farmers using Gemma 4. During that process I made every possible mistake - downloaded the wrong model variant, crashed my system, misunderstood what "E4B" meant, and spent hours confused about why multimodal wasn't working.

This article is what I wish I had read before starting.

By the end you'll know exactly which Gemma 4 model to use for your use case, what makes the architecture genuinely different from anything before it, and why this family of models is a bigger deal than most developers realize.

First: Why Gemma 4 Is Different

Most open-source models are powerful but come with a catch - they either need the cloud (OpenAI, Anthropic), require massive hardware (LLaMA 70B), or sacrifice too much quality for local use (older 2B/7B models).

Gemma 4 breaks that tradeoff.

The 31B model currently ranks #3 among all open models globally on the Arena AI leaderboard. The 26B MoE ranks #6. Both run on consumer hardware. Both are Apache 2.0 licensed - meaning you can use them commercially, modify them, and deploy them anywhere, for free.

But more importantly: Gemma 4 processes text, images, video, and audio natively. Not bolted-on, not a separate pipeline - natively, in a single model call. That changes what's possible to build locally.

Decoding the Model Names: E2B, E4B, 26B A4B, 31B

This is the part that confuses everyone. Let's decode it once and for all.

The "E" Models: Edge

The word "effective" is doing real work here. These aren't traditional dense models - they use architectural tricks (Per-Layer Embeddings) to punch above their weight class. The "E" signals they're built for edge deployment: phones, Raspberry Pi, laptops, embedded hardware.

Both E2B and E4B support:

• Text + Image + Audio input natively
• Up to 128K token context window
• Fully offline via Ollama
• No GPU required (CPU inference possible)

The Workstation Models: 26B and 31B

The 26B A4B uses a Mixture of Experts (MoE) architecture — it has 26B parameters but only activates 4B of them for any given token. This means it runs at roughly the speed of a 4B model while producing quality closer to a full 26B model. That "A" stands for Active.

Both workstation models support:

• Text + Image + Video (up to 60 seconds at 1fps) input
• Up to 256K token context window
• Hybrid attention (local sliding window + full global)

The Decision Table

When I built KhetAI I chose E4B — it needed to run on a mid-range Windows laptop with a consumer GPU, handle crop photos, and respond in 8 Indian languages. E2B was too limited, 26B/31B too heavy for the target hardware.

The Architecture That Makes It Work

Hybrid Attention: Fast AND Deep

Traditional transformers use full attention - every token attends to every other token. This is expensive. Gemma 4 uses a hybrid attention mechanism that interleaves:

• Local sliding window attention for nearby tokens (fast, memory-efficient)
• Full global attention for the final layer (captures long-range dependencies)

This gives you the processing speed of a lightweight model without losing the deep contextual understanding you need for complex tasks. The final layer is always global — ensuring nothing important gets missed.

Proportional RoPE (p-RoPE)

For long contexts (up to 256K tokens), Gemma 4 uses Proportional RoPE — a smarter positional encoding that scales proportionally with sequence length. Combined with unified Keys and Values in global attention layers, this dramatically reduces memory usage at long contexts compared to standard transformers.

Variable-Resolution Vision

When you send an image to Gemma 4, it doesn't resize everything to a fixed resolution. It uses a dynamic number of soft tokens fitted exactly to the image's content and resolution. A high-detail photo gets more tokens than a simple diagram. This matters enormously for real-world tasks like document analysis, medical imaging, or - in my case - crop disease detection where fine texture details are diagnostic signals.

Per-Layer Embeddings (PLE) in Edge Models

The E2B and E4B models use Per-Layer Embeddings instead of shared embeddings across all layers. This lets them carry more representational richness per parameter - which is why they're called "Effective" rather than just "2B" and "4B". They genuinely outperform models of equivalent raw parameter count.

The Thinking Mode: Built-in Reasoning

Every Gemma 4 model has a configurable thinking mode - the model can reason step-by-step before producing its final answer.

In Ollama, you enable it like this:

response = ollama.chat(
    model="gemma4:e4b",
    messages=[{"role": "user", "content": "your prompt"}],
    options={"thinking": True}  # enables step-by-step reasoning
)

This is particularly powerful for:

• Diagnosis tasks - reasoning through symptoms before concluding
• Code debugging - thinking through the logic before suggesting a fix
• Multi-step math - working through the problem before giving an answer
• Agentic workflows - planning before acting

The thinking tokens are separate from the response — you get clean output plus the reasoning chain if you want to inspect it.

Running Gemma 4 Locally: Complete Setup

1. Install Ollama

Download from ollama.com - available for Mac, Windows, Linux.

2. Pull Your Model

# Edge models (recommended for most developers)
ollama pull gemma4:e2b   # ~7GB - phones, Pi, very low-end hardware
ollama pull gemma4:e4b   # ~10GB - any modern laptop ✅ recommended start

# Workstation models (need a good GPU)
ollama pull gemma4:26b   # ~16GB - MoE, great quality/speed balance
ollama pull gemma4:31b   # ~20GB - maximum quality

3. Basic Text Usage

import ollama

response = ollama.chat(
    model="gemma4:e4b",
    messages=[
        {"role": "user", "content": "Explain the difference between MoE and Dense transformers"}
    ],
    options={"temperature": 0.7}
)

print(response["message"]["content"])

4. Multimodal Usage (Image + Text)

import ollama
import base64

# Load and encode the image
with open("crop_photo.jpg", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode("utf-8")

response = ollama.chat(
    model="gemma4:e4b",
    messages=[
        {
            "role": "user",
            "content": "What disease does this crop have? Suggest treatment.",
            "images": [image_b64],  # pass base64 encoded image
        }
    ],
    options={"temperature": 0.2}  # low temp for factual tasks
)

print(response["message"]["content"])

That's it. No separate vision model. No preprocessing pipeline. One call.

5. Structured JSON Output

For applications, you want reliable structured output. Use a system prompt with low temperature:

import ollama
import json

SYSTEM = """Always respond ONLY with valid JSON. No preamble, no markdown.
Format: {"answer": "...", "confidence": "High/Medium/Low", "reasoning": "..."}"""

response = ollama.chat(
    model="gemma4:e4b",
    messages=[{"role": "user", "content": "Is this tomato leaf diseased?", "images": [image_b64]}],
    system=SYSTEM,
    options={"temperature": 0.1}
)

result = json.loads(response["message"]["content"])

6. Using the 128K Context Window

The E4B's 128K context window means you can pass entire codebases, long documents, or full conversation histories in a single call:

# Pass an entire document for analysis
with open("research_paper.txt", "r") as f:
    document = f.read()

response = ollama.chat(
    model="gemma4:e4b",
    messages=[
        {
            "role": "user",
            "content": f"Summarize the key findings and methodology:\n\n{document}"
        }
    ]
)

Choosing Temperature: A Practical Guide

Temperature is the most impactful parameter after model selection:

What This Means for Developers

Here's the shift Gemma 4 represents, stated plainly:

Before Gemma 4, building a multimodal, multilingual, locally-running AI application required:

• A separate vision model (CLIP, LLaVA, etc.)
• A separate language model
• A translation/multilingual layer
• A cloud API for inference at any serious quality level
• Significant engineering to glue it all together

With Gemma 4 E4B, you get all of that in a single 10GB model that runs on a laptop, costs nothing to operate, and sends zero data to any server.

That's not an incremental improvement. That's a different category.

For developers building for markets with unreliable internet — rural areas, developing countries, privacy-sensitive domains (medical, legal, financial), air-gapped enterprise environments - Gemma 4 is the first model that makes a fully capable local AI genuinely feasible.

What I Learned Building With It

I built KhetAI - an offline crop diagnostic tool for Indian farmers — using gemma4:e4b. A farmer uploads a crop photo, asks a question in Hindi or Kannada, and gets a structured diagnosis with treatment steps and local remedies. Zero internet. Zero cloud. Zero cost to operate.

The things that surprised me:

Multilingual works without any configuration. I just tell the model the language in the system prompt. It responds in that language natively — Hindi, Kannada, Tamil, Telugu. No translation layer, no separate multilingual model.

Image quality really matters. The dynamic soft-token system means a blurry photo gives fewer tokens to work with. For diagnostic tasks, tell your users to take clear, well-lit photos.

Low temperature is your friend for structured output. Setting temperature to 0.2 and adding "respond ONLY with JSON" to the system prompt gives remarkably consistent structured responses.

First inference is slow, subsequent ones are fast. The model loads into GPU memory on the first call. After that, it stays loaded and responses come quickly. Tell your users to expect a 30-90 second wait on the first analysis.

The Bigger Picture

There are 400 million Gemma model downloads. Over 100,000 community fine-tuned variants. Google calls this the "Gemmaverse."

The Apache 2.0 license means none of that growth has a ceiling. No usage fees, no terms that restrict commercial use, no platform lock-in. A developer in Bengaluru building for Indian farmers and a startup in São Paulo building for Brazilian doctors and a researcher at a university in Nairobi are all working with the same foundation model, improving it, specializing it, and sharing their work.

That's what open-weight AI at this capability level actually means. Not just "a free model" — a foundation that any developer anywhere can build on without asking permission.

Gemma 4 is the clearest example yet of that becoming real.

Quick Reference

# Install Ollama: https://ollama.com

# Pull recommended model for most developers
ollama pull gemma4:e4b

# Test immediately
ollama run gemma4:e4b "What is the Mixture of Experts architecture?"

# Python
pip install ollama

import ollama

# Text
r = ollama.chat(model="gemma4:e4b", messages=[{"role":"user","content":"Hello"}])

# Image
r = ollama.chat(model="gemma4:e4b", messages=[{"role":"user","content":"Describe this","images":["base64string"]}])

# Structured output
r = ollama.chat(model="gemma4:e4b", messages=[...], system="Return only JSON", options={"temperature":0.1})

Built KhetAI with Gemma 4 E4B for the Gemma 4 Good Hackathon 2026. GitHub: https://github.com/Tech-Psycho95/KhetAI

gemma, ai, python, ollama