<div align="center"> <img src="assets/shimmy-logo.png" alt="Shimmy Logo" width="300" height="auto" />

The Lightweight OpenAI API Server

🔒 Local Inference Without Dependencies 🚀

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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:/