April 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac mini

A comprehensive guide to setting up Gemma 4 26B on Apple Silicon Mac mini, featuring optimizations for MLX, NVFP4 format, and advanced memory management techniques.

Mac mini with Apple Silicon (M1/M2/M3/M4/M5)
At least 24GB unified memory for Gemma 4 26B
macOS with Homebrew installed

Install the Ollama macOS app via Homebrew cask (includes auto-updates and MLX backend):

brew install --cask ollama-app

This installs:

Ollama.app

in/Applications/

ollama

CLI at/opt/homebrew/bin/ollama

open -a Ollama

The Ollama icon will appear in the menu bar. Wait a few seconds for the server to initialize.

Verify it's running:

ollama list

ollama pull gemma4:26b

This downloads ~17GB. Verify:

ollama list
# NAME ID SIZE MODIFIED
# gemma4:26b 5571076f3d70 17 GB ...

ollama run gemma4:26b "Hello, what model are you?"

Check that it's using GPU acceleration:

ollama ps
# Should show CPU/GPU split, e.g. 14%/86% CPU/GPU

Click the Ollama icon in the menu bar > Launch at Login (enable it).

Alternatively, go to System Settings > General > Login Items and add Ollama.

Create a launch agent that loads the model into memory after Ollama starts and keeps it warm:

cat << 'EOF' > ~/Library/LaunchAgents/com.ollama.preload-gemma4.plist
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>Label</key>
<string>com.ollama.preload-gemma4</string>
<key>ProgramArguments</key>
<array>
<string>/opt/homebrew/bin/ollama</string>
<string>run</string>
<string>gemma4:26b</string>
<string></string>
</array>
<key>RunAtLoad</key>
<true/>
<key>StartInterval</key>
<integer>300</integer>
<key>StandardOutPath</key>
<string>/tmp/ollama-preload.log</string>
<key>StandardErrorPath</key>
<string>/tmp/ollama-preload.log</string>
</dict>
</plist>
EOF

Load the agent:

launchctl load ~/Library/LaunchAgents/com.ollama.preload-gemma4.plist

This sends an empty prompt to ollama run

every 5 minutes, keeping the model warm in memory.

By default, Ollama unloads models after 5 minutes of inactivity. To keep them loaded forever:

launchctl setenv OLLAMA_KEEP_ALIVE "-1"

Then restart Ollama for the change to take effect.

Note:This environment variable is session-scoped. To persist across reboots, addexport OLLAMA_KEEP_ALIVE="-1"

to your~/.zshrc

, or set it via a dedicated launch agent.

# Check Ollama server is running
ollama list
# Check model is loaded in memory
ollama ps
# Check launch agent is registered
launchctl list | grep ollama

Expected output from ollama ps

NAME ID SIZE PROCESSOR CONTEXT UNTIL
gemma4:26b 5571076f3d70 20 GB 14%/86% CPU/GPU 4096 Forever

Ollama exposes a local API at http://localhost:11434

. Use it with coding agents:

# Chat completion (OpenAI-compatible)
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "gemma4:26b",
"messages": [{"role": "user", "content": "Hello"}]
}'

| Command | Description | |---|---| ollama list | List downloaded models | ollama ps | Show running models & memory usage | ollama run gemma4:26b | Interactive chat | ollama stop gemma4:26b | Unload model from memory | ollama pull gemma4:26b | Update model to latest version | ollama rm gemma4:26b | Delete model |

# Remove the preload agent
launchctl unload ~/Library/LaunchAgents/com.ollama.preload-gemma4.plist
rm ~/Library/LaunchAgents/com.ollama.preload-gemma4.plist
# Uninstall Ollama
brew uninstall --cask ollama-app

On Apple Silicon, Ollama automatically uses Apple's MLX framework for faster inference — no manual configuration needed. M5/M5 Pro/M5 Max chips get additional acceleration via GPU Neural Accelerators. M4 and earlier still benefit from general MLX speedups.

Ollama now leverages NVIDIA's NVFP4 format to maintain model accuracy while reducing memory bandwidth and storage requirements for inference workloads. As more inference providers scale inference using NVFP4 format, this allows Ollama users to share the same results as they would in a production environment. It further opens up Ollama to run models optimized by NVIDIA's model optimizer.

**Lower memory utilization:**Ollama reuses its cache across conversations, meaning less memory utilization and more cache hits when branching with a shared system prompt — especially useful with tools like Claude Code.**Intelligent checkpoints:**Ollama stores snapshots of its cache at intelligent locations in the prompt, resulting in less prompt processing and faster responses.**Smarter eviction:**Shared prefixes survive longer even when older branches are dropped.

**Memory:**Gemma 4 26B uses ~20GB when loaded. On a 24GB Mac mini, this leaves ~4GB for the system — close memory-heavy apps before running.