The Lightweight OpenAI API Server

🔒 Local Inference Without Dependencies 🚀

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

🚀 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 🙏

Shimmy is a 4.8MB 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.

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

# 1) Install + run
cargo install shimmy --features huggingface
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'

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

• 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)

• 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

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

• 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

# RECOMMENDED: Use pre-built binary (no build dependencies required)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy.exe -o shimmy.exe

# OR: Install from source with MOE support
# First install build dependencies:
winget install LLVM.LLVM
# Then install shimmy with MOE:
cargo install shimmy --features moe

# For CUDA + MOE hybrid processing:
cargo install shimmy --features llama-cuda,moe

⚠️ Windows Notes:

• Pre-built binary recommended to avoid build dependency issues
• MSVC compatibility: Uses shimmy-llama-cpp-2 packages for better Windows support
• If Windows Defender flags the binary, add an exclusion or use cargo install
• For cargo install: Install LLVM first to resolve libclang.dll errors

# Install from crates.io
cargo install shimmy --features huggingface

Shimmy supports multiple GPU backends for accelerated inference:

# Show detected GPU backends
shimmy gpu-info

• GPU backends are automatically detected at runtime
• Falls back to CPU if GPU is unavailable
• Multiple backends can be compiled in, best one selected automatically
• Use --gpu-backend <backend> to force specific backend

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/

# 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.

• 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)

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

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

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

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

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

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.

• 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

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

• 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™

• 🧠 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

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

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

🔥 Hacker News • Front Page Again • IPE Newsletter

Companies: Need invoicing? Email michaelallenkuykendall@gmail.com

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

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.

MIT License - forever and always.

Philosophy: Infrastructure should be invisible. Shimmy is infrastructure.

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

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