How to Use Local AI Models with Shakespeare
Want complete control over your AI-powered website building? Local AI models offer enhanced privacy, faster responses, and dramatically reduced costs in your development workflow.
Want complete control over your AI-powered website building? Local AI models offer enhanced privacy, faster responses, and dramatically reduced costs in your development workflow. With Shakespeare's support for local models including GPT-OSS, DeepSeek-R1, Gemma 3 and other models running on your machine, you can build websites entirely offline while keeping your projects and data completely private.
Why Choose Local AI Models?
🔒 Complete Privacy
- • No data ever leaves your machine
- • Perfect for sensitive business projects
- • Full control over your intellectual property
- • No concerns about terms of service changes
💰 Zero Ongoing Costs
- • Pay once for hardware, use forever
- • No per-request charges
- • No monthly subscriptions
- • Unlimited usage without budget worries
🎛️ Full Customization
- • Fine-tune models for your specific needs
- • Complete control over model behavior
- • No rate limits or usage restrictions
- • Experiment freely without costs
Getting Started: Installing Ollama
System Requirements
Minimum
8GB RAM, modern CPU
Recommended
16GB+ RAM, GPU with 8GB+ VRAM
Optimal
32GB+ RAM, high-end GPU
Installation Steps
🍎 macOS Installation
# Install via Homebrew
brew install ollama
# Or download from ollama.ai
curl -fsSL https://ollama.ai/install.sh | sh🐧 Linux Installation
# Install via curl
curl -fsSL https://ollama.ai/install.sh | sh
# Or use package manager
sudo apt install ollama # Ubuntu/Debian
sudo pacman -S ollama # Arch Linux🪟 Windows Installation
- 1. Download the installer from ollama.ai
- 2. Run the installer as administrator
- 3. Restart your system after installation
Choosing the Right Model
For Website Building: Top Local Model Recommendations
GPT-OSS - OpenAI's Open-Weight Powerhouse
ollama pull gpt-ossBest for:
Powerful reasoning, agentic tasks, versatile developer use cases
Trade-offs:
✓ Excellent at complex web development
⚠ Requires decent hardware
DeepSeek-R1 - Enterprise-Grade Reasoning
ollama pull deepseek-r1Best for:
Open reasoning with performance approaching O3 and Gemini 2.5 Pro
Trade-offs:
✓ Leading-edge reasoning capabilities
⚠ Slower inference due to reasoning depth
Gemma 3 - Google's Single-GPU Solution
ollama pull gemma3Best for:
Google's most capable model that runs on a single GPU
Trade-offs:
✓ Excellent performance on consumer hardware
⚠ May need more guidance for complex tasks
Quick Model Comparison
| Model | Size | RAM Required | Speed | Code Quality | Content Quality |
|---|---|---|---|---|---|
| GPT-OSS | 8GB | 16GB | Fast | Excellent | Excellent |
| DeepSeek-R1 | 7GB | 16GB | Medium | Excellent | Very Good |
| Gemma 3 | 4GB | 8GB | Very Fast | Very Good | Good |
Configuring CORS for Browser Access
Important: CORS Configuration Required
Since Shakespeare runs in your browser, you need to configure Ollama to accept cross-origin requests (CORS). This is a crucial step that allows Shakespeare to communicate with your local Ollama instance.
Checking CORS Status
First, verify if CORS is already enabled:
curl -X OPTIONS http://localhost:11434 -H "Origin: http://example.com" -H "Access-Control-Request-Method: GET" -IIf you see HTTP/1.1 403 Forbidden, CORS is not enabled and needs configuration.
Enabling CORS by Platform
🍎 Enabling CORS on macOS
# Allow all origins (easiest for local development)
launchctl setenv OLLAMA_ORIGINS "*"
# Or specify specific origins for better security
launchctl setenv OLLAMA_ORIGINS "localhost:3000,localhost:5173,shakespeare.app"
# Optional: Make Ollama accessible on your network
launchctl setenv OLLAMA_HOST "0.0.0.0"
# Restart Ollama for changes to take effect🐧 Enabling CORS on Linux
Edit the Ollama service configuration:
sudo systemctl edit ollama.serviceAdd these environment variables:
[Service]
Environment="OLLAMA_HOST=0.0.0.0"
Environment="OLLAMA_ORIGINS=*"Then restart the service:
sudo service ollama restart🪟 Enabling CORS on Windows
- 1. Open System Properties → Environment Variables
- 2. Add new system variables:
- •
OLLAMA_ORIGINSwith value*(or specific origins) - •
OLLAMA_HOSTwith value0.0.0.0(optional, for network access) - 3. Restart Ollama from the system tray
Verifying CORS Configuration
After configuration, test again:
curl -X OPTIONS http://localhost:11434 -H "Origin: http://example.com" -H "Access-Control-Request-Method: GET" -ISuccess looks like:
HTTP/1.1 204 No Content
Access-Control-Allow-Origin: *Configuring Local Models in Shakespeare
Start Ollama Service
# Start Ollama server (with CORS already configured)
ollama serve
# The service will run on http://localhost:11434Test Your Model
ollama run gpt-oss "Write a simple HTML page with a header"Configure in Shakespeare
- 1. Open Shakespeare and go to Settings > AI Settings
- 2. Scroll to "Add Custom Provider"
- 3. Click to expand the custom provider section
- 4. Enter the following configuration:
- • Provider Name: "Ollama Local"
- • API Endpoint:
http://localhost:11434 - • Model Name:
gpt-oss(or your chosen model) - • API Type: "Ollama"
Test the Connection
- 1. Click "Test Connection" to verify setup
- 2. If successful, click "Add" to save the provider
- 3. Select your local model from the configured providers list
The Bottom Line
🚀 Local Models with Shakespeare Provide
Ready to supercharge your development workflow? Start with GPT-OSS and experience the benefits of local AI models in Shakespeare.
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