The Lightweight OpenAI API Server

🔒 Local Inference Without Dependencies 🚀

Shimmy will be free forever. No asterisks. No "free for now." No pivot to paid.

💝 Support Shimmy's Growth

🚀 If Shimmy helps you, consider sponsoring — 100% of support goes to keeping it free forever.

• $5/month: Coffee tier ☕ - Eternal gratitude + sponsor badge
• $25/month: Bug prioritizer 🐛 - Priority support + name in SPONSORS.md
• $100/month: Corporate backer 🏢 - Logo placement + monthly office hours
• $500/month: Infrastructure partner 🚀 - Direct support + roadmap input

🎯 Become a Sponsor | See our amazing sponsors 🙏

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Drop-in OpenAI API Replacement for Local LLMs

Shimmy is a single-binary that provides 100% OpenAI-compatible endpoints for GGUF models. Point your existing AI tools to Shimmy and they just work — locally, privately, and free.

🎉 NEW in v1.9.0: One download, all GPU backends included! No compilation, no backend confusion - just download and run.

Developer Tools

Whether you're forking Shimmy or integrating it as a service, we provide complete documentation and integration templates.

Try it in 30 seconds

# 1) Download pre-built binary (includes all GPU backends)
# Windows:
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-windows-x86_64.exe -o shimmy.exe
./shimmy.exe serve &

# Linux:
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-linux-x86_64 -o shimmy && chmod +x shimmy
./shimmy serve &

# macOS (Apple Silicon):
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-macos-arm64 -o shimmy && chmod +x shimmy
./shimmy serve &

# 2) See models and pick one
./shimmy list

# 3) Smoke test the OpenAI API
curl -s http://127.0.0.1:11435/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
        "model":"REPLACE_WITH_MODEL_FROM_list",
        "messages":[{"role":"user","content":"Say hi in 5 words."}],
        "max_tokens":32
      }' | jq -r '.choices[0].message.content'

🚀 Compatible with OpenAI SDKs and Tools

No code changes needed - just change the API endpoint:

• Any OpenAI client: Python, Node.js, curl, etc.
• Development applications: Compatible with standard SDKs
• VSCode Extensions: Point to http://localhost:11435
• Cursor Editor: Built-in OpenAI compatibility
• Continue.dev: Drop-in model provider

Use with OpenAI SDKs

• Node.js (openai v4)

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "http://127.0.0.1:11435/v1",
  apiKey: "sk-local", // placeholder, Shimmy ignores it
});

const resp = await openai.chat.completions.create({
  model: "REPLACE_WITH_MODEL",
  messages: [{ role: "user", content: "Say hi in 5 words." }],
  max_tokens: 32,
});

console.log(resp.choices[0].message?.content);

• Python (openai>=1.0.0)

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:11435/v1", api_key="sk-local")

resp = client.chat.completions.create(
    model="REPLACE_WITH_MODEL",
    messages=[{"role": "user", "content": "Say hi in 5 words."}],
    max_tokens=32,
)

print(resp.choices[0].message.content)

⚡ Zero Configuration Required

• Automatically finds models from Hugging Face cache, Ollama, local dirs
• Auto-allocates ports to avoid conflicts
• Auto-detects LoRA adapters for specialized models
• Just works - no config files, no setup wizards

🧠 Advanced MOE (Mixture of Experts) Support

Run 70B+ models on consumer hardware with intelligent CPU/GPU hybrid processing:

• 🔄 CPU MOE Offloading: Automatically distribute model layers across CPU and GPU
• 🧮 Intelligent Layer Placement: Optimizes which layers run where for maximum performance
• 💾 Memory Efficiency: Fit larger models in limited VRAM by using system RAM strategically
• ⚡ Hybrid Acceleration: Get GPU speed where it matters most, CPU reliability everywhere else
• 🎛️ Configurable: --cpu-moe and --n-cpu-moe flags for fine control

# Enable MOE CPU offloading during installation
cargo install shimmy --features moe

# Run with MOE hybrid processing
shimmy serve --cpu-moe --n-cpu-moe 8

# Automatically balances: GPU layers (fast) + CPU layers (memory-efficient)

Perfect for: Large models (70B+), limited VRAM systems, cost-effective inference

🎯 Perfect for Local Development

• Privacy: Your code never leaves your machine
• Cost: No API keys, no per-token billing
• Speed: Local inference, sub-second responses
• Reliability: No rate limits, no downtime

Quick Start (30 seconds)

Installation

✨ v1.9.0 NEW: Download pre-built binaries with ALL GPU backends included!

📥 Pre-Built Binaries (Recommended - Zero Dependencies)

Pick your platform and download - no compilation needed:

# Windows x64 (includes CUDA + Vulkan + OpenCL)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-windows-x86_64.exe -o shimmy.exe

# Linux x86_64 (includes CUDA + Vulkan + OpenCL)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-linux-x86_64 -o shimmy && chmod +x shimmy

# macOS ARM64 (includes MLX for Apple Silicon)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-macos-arm64 -o shimmy && chmod +x shimmy

# macOS Intel (CPU-only)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-macos-intel -o shimmy && chmod +x shimmy

# Linux ARM64 (CPU-only)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-linux-aarch64 -o shimmy && chmod +x shimmy

That's it! Your GPU will be detected automatically at runtime.

🛠️ Build from Source (Advanced)

Want to customize or contribute?

# Basic installation (CPU only)
cargo install shimmy --features huggingface

# Kitchen Sink builds (what pre-built binaries use):
# Windows/Linux x64:
cargo install shimmy --features huggingface,llama,llama-cuda,llama-vulkan,llama-opencl,vision

# macOS ARM64:
cargo install shimmy --features huggingface,llama,mlx,vision

# CPU-only (any platform):
cargo install shimmy --features huggingface,llama,vision

⚠️ Build Notes:

• Windows: Install LLVM first for libclang.dll
• Recommended: Use pre-built binaries to avoid dependency issues
• Advanced users only: Building from source requires C++ compiler + CUDA/Vulkan SDKs

GPU Acceleration

✨ NEW in v1.9.0: One binary per platform with automatic GPU detection!

⚠️ IMPORTANT - Vision Feature Performance:
CPU-based vision inference (MiniCPM-V) is 5-10x slower than GPU acceleration.
CPU: 15-45 seconds per image | GPU (CUDA/Vulkan): 2-8 seconds per image
For production vision workloads, GPU acceleration is strongly recommended.

📥 Download Pre-Built Binaries (Recommended)

No compilation needed! Each binary includes ALL GPU backends for your platform:

Platform	Download	GPU Support	Auto-Detects
Windows x64	shimmy-windows-x86_64.exe	CUDA + Vulkan + OpenCL	✅
Linux x86_64	shimmy-linux-x86_64	CUDA + Vulkan + OpenCL	✅
macOS ARM64	shimmy-macos-arm64	MLX (Apple Silicon)	✅
macOS Intel	shimmy-macos-intel	CPU only	N/A
Linux ARM64	shimmy-linux-aarch64	CPU only	N/A

How it works: Download one file, run it. Shimmy automatically detects and uses your GPU!

# Windows example
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-windows-x86_64.exe -o shimmy.exe
./shimmy.exe serve --gpu-backend auto  # Auto-detects CUDA/Vulkan/OpenCL

# Linux example  
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-linux-x86_64 -o shimmy
chmod +x shimmy
./shimmy serve --gpu-backend auto  # Auto-detects CUDA/Vulkan/OpenCL

# macOS ARM64 example
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy-macos-arm64 -o shimmy
chmod +x shimmy  
./shimmy serve  # Auto-detects MLX on Apple Silicon

🎯 GPU Auto-Detection

Shimmy uses intelligent GPU detection with this priority order:

1. CUDA (NVIDIA GPUs via nvidia-smi)
2. Vulkan (Cross-platform GPUs via vulkaninfo)
3. OpenCL (AMD/Intel GPUs via clinfo)
4. MLX (Apple Silicon via system detection)
5. CPU (Fallback if no GPU detected)

No manual configuration needed! Just run with --gpu-backend auto (default).

🔧 Manual Backend Override

Want to force a specific backend? Use the --gpu-backend flag:

# Auto-detect (default - recommended)
shimmy serve --gpu-backend auto

# Force CPU (for testing or compatibility)
shimmy serve --gpu-backend cpu

# Force CUDA (NVIDIA GPUs only)
shimmy serve --gpu-backend cuda

# Force Vulkan (AMD/Intel/Cross-platform)
shimmy serve --gpu-backend vulkan

# Force OpenCL (AMD/Intel alternative)
shimmy serve --gpu-backend opencl

🛡️ Error Handling & Robustness: If you force an unavailable backend (e.g., --gpu-backend cuda on AMD GPU), Shimmy will:

1. ✅ Display clear error message explaining the issue
2. ✅ Automatically fallback to next available backend in priority order
3. ✅ Log which backend was actually used (check with --verbose)
4. ✅ Continue serving requests (graceful degradation, no crashes)
5. ✅ Support environment variable override: SHIMMY_GPU_BACKEND=cuda

Common scenarios:

• --gpu-backend cuda on non-NVIDIA → Falls back to Vulkan or OpenCL
• --gpu-backend vulkan without drivers → Falls back to OpenCL or CPU
• --gpu-backend invalid → Clear error + fallback to auto-detection
• No GPU detected → Runs on CPU with performance warning

Environment Variable: Set SHIMMY_GPU_BACKEND=cuda to override default without CLI flags.

🔍 Check GPU Support

# Show detected GPU backends
shimmy gpu-info

# Check which backend is being used
shimmy serve --gpu-backend auto --verbose

⚡ Binary Sizes

• GPU-enabled binaries (Windows/Linux x64, macOS ARM64): ~40-50MB
• CPU-only binaries (macOS Intel, Linux ARM64): ~20-30MB

Trade-off: Slightly larger binaries for zero compilation and automatic GPU detection.

🛠️ Build from Source (Advanced)

Want to customize or contribute? Build from source:

• Multiple backends can be compiled in, best one selected automatically
• Use --gpu-backend <backend> to force specific backend

Get Models

Shimmy auto-discovers models from:

• Hugging Face cache: ~/.cache/huggingface/hub/
• Ollama models: ~/.ollama/models/
• Local directory: ./models/
• Environment: SHIMMY_BASE_GGUF=path/to/model.gguf

# Download models that work out of the box
huggingface-cli download microsoft/Phi-3-mini-4k-instruct-gguf --local-dir ./models/
huggingface-cli download bartowski/Llama-3.2-1B-Instruct-GGUF --local-dir ./models/

Start Server

# Auto-allocates port to avoid conflicts
shimmy serve

# Or use manual port
shimmy serve --bind 127.0.0.1:11435

Point your development tools to the displayed port — VSCode Copilot, Cursor, Continue.dev all work instantly.

📦 Download & Install

Package Managers

• Rust: cargo install shimmy --features moe (recommended)
• Rust (basic): cargo install shimmy
• VS Code: Shimmy Extension
• Windows MSVC: Uses shimmy-llama-cpp-2 packages for better compatibility
• npm: npm install -g shimmy-js (planned)
• Python: pip install shimmy (planned)

Direct Downloads

• GitHub Releases: Latest binaries
• Docker: docker pull shimmy/shimmy:latest (coming soon)

🍎 macOS Support

Full compatibility confirmed! Shimmy works flawlessly on macOS with Metal GPU acceleration.

# Install dependencies
brew install cmake rust

# Install shimmy
cargo install shimmy

✅ Verified working:

• Intel and Apple Silicon Macs
• Metal GPU acceleration (automatic)
• MLX native acceleration for Apple Silicon
• Xcode 17+ compatibility
• All LoRA adapter features

Integration Examples

VSCode Copilot

{
  "github.copilot.advanced": {
    "serverUrl": "http://localhost:11435"
  }
}

Continue.dev

{
  "models": [{
    "title": "Local Shimmy",
    "provider": "openai",
    "model": "your-model-name",
    "apiBase": "http://localhost:11435/v1"
  }]
}

Cursor IDE

Works out of the box - just point to http://localhost:11435/v1

Why Shimmy Will Always Be Free

I built Shimmy to retain privacy-first control on my AI development and keep things local and lean.

This is my commitment: Shimmy stays MIT licensed, forever. If you want to support development, sponsor it. If you don't, just build something cool with it.

💡 Shimmy saves you time and money. If it's useful, consider sponsoring for $5/month — less than your Netflix subscription, infinitely more useful for developers.

API Reference

Endpoints

• GET /health - Health check
• POST /v1/chat/completions - OpenAI-compatible chat
• GET /v1/models - List available models
• POST /api/generate - Shimmy native API
• GET /ws/generate - WebSocket streaming

CLI Commands

shimmy serve                    # Start server (auto port allocation)
shimmy serve --bind 127.0.0.1:8080  # Manual port binding
shimmy serve --cpu-moe --n-cpu-moe 8  # Enable MOE CPU offloading
shimmy list                     # Show available models (LLM-filtered)
shimmy discover                 # Refresh model discovery
shimmy generate --name X --prompt "Hi"  # Test generation
shimmy probe model-name         # Verify model loads
shimmy gpu-info                 # Show GPU backend status

Technical Architecture

• Rust + Tokio: Memory-safe, async performance
• llama.cpp backend: Industry-standard GGUF inference
• OpenAI API compatibility: Drop-in replacement
• Dynamic port management: Zero conflicts, auto-allocation
• Zero-config auto-discovery: Just works™

🚀 Advanced Features

• 🧠 MOE CPU Offloading: Hybrid GPU/CPU processing for large models (70B+)
• 🎯 Smart Model Filtering: Automatically excludes non-language models (Stable Diffusion, Whisper, CLIP)
• 🛡️ 6-Gate Release Validation: Constitutional quality limits ensure reliability
• ⚡ Smart Model Preloading: Background loading with usage tracking for instant model switching
• 💾 Response Caching: LRU + TTL cache delivering 20-40% performance gains on repeat queries
• 🚀 Integration Templates: One-command deployment for Docker, Kubernetes, Railway, Fly.io, FastAPI, Express
• 🔄 Request Routing: Multi-instance support with health checking and load balancing
• 📊 Advanced Observability: Real-time metrics with self-optimization and Prometheus integration
• 🔗 RustChain Integration: Universal workflow transpilation with workflow orchestration

Community & Support

• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📖 Documentation: docs/ • Engineering Methodology • OpenAI Compatibility Matrix • Benchmarks (Reproducible)
• 💝 Sponsorship: GitHub Sponsors

Star History

🚀 Momentum Snapshot

📦 Sub-5MB single binary (142x smaller than Ollama) 🌟 stars and climbing fast ⏱ <1s startup 🦀 100% Rust, no Python

📰 As Featured On

🔥 Hacker News • Front Page Again • IPE Newsletter

Companies: Need invoicing? Email [email protected]

⚡ Performance Comparison

Tool	Binary Size	Startup Time	Memory Usage	OpenAI API
Shimmy	4.8MB	<100ms	50MB	100%
Ollama	680MB	5-10s	200MB+	Partial
llama.cpp	89MB	1-2s	100MB	Via llama-server

Quality & Reliability

Shimmy maintains high code quality through comprehensive testing:

• Comprehensive test suite with property-based testing
• Automated CI/CD pipeline with quality gates
• Runtime invariant checking for critical operations
• Cross-platform compatibility testing

Development Testing

Run the complete test suite:

# Using cargo aliases
cargo test-quick           # Quick development tests

# Using Makefile  
make test                  # Full test suite
make test-quick            # Quick development tests

See our testing approach for technical details.

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License & Philosophy

MIT License - forever and always.

Philosophy: Infrastructure should be invisible. Shimmy is infrastructure.

Testing Philosophy: Reliability through comprehensive validation and property-based testing.

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Forever maintainer: Michael A. Kuykendall Promise: This will never become a paid product Mission: Making local model inference simple and reliable