Adds shimmy to the LLMOps inference tools section.

Project Details:

• Repository: https://github.com/Michael-A-Kuykendall/shimmy
• Stars: 2,918 ⭐ (strong community adoption)
• Language: Rust
• License: MIT

Why Shimmy enhances Awesome-LLMOps:

Production LLMOps Benefits:

• Zero-dependency deployment: Single binary eliminates Python environment complexity
• OpenAI API compatibility: Seamless integration with existing LLMOps workflows and tools
• Hot model swapping: Zero-downtime model updates critical for production operations
• Auto-discovery: Simplified model management and deployment automation
• Resource efficiency: Rust performance reduces infrastructure costs

LLMOps Integration Points:

• CI/CD Pipelines: Single binary deployment simplifies containerization and orchestration
• Monitoring & Observability: OpenAI-compatible metrics and logging endpoints
• Multi-model Serving: GGUF + SafeTensors support for diverse model ecosystems
• Edge & Cloud: Consistent API across deployment environments
• Cost Optimization: Reduced resource footprint vs Python-based inference servers

Technical Advantages for LLMOps:

• Model Format Support: Modern GGUF and SafeTensors formats
• API Standards: OpenAI-compatible REST endpoints
• Performance: Rust implementation optimized for production throughput
• Reliability: Memory-safe inference serving with minimal attack surface
• Scalability: Lightweight deployment suitable for horizontal scaling

Positioning: Shimmy sits alongside other production inference solutions like Ollama, vLLM, and text-generation-inference, offering a unique Rust-native approach that eliminates Python dependencies while maintaining OpenAI compatibility.

This addition strengthens the LLMOps toolkit by providing teams with a robust, dependency-light option for LLM inference serving that integrates seamlessly into modern MLOps pipelines.