Key Takeaways

In this module, we’ve covered the essential foundations of LLM serving with Ray Serve LLM:

1. Understanding LLM Serving: How LLMs generate text through prefill and decode phases

2. Key Optimizations: KV caching, paged attention, and continuous batching

3. Challenges: Memory management, latency, scalability, and cost optimization

4. Ray Serve LLM Architecture: Three-component solution with Ray Serve, vLLM, and Anyscale

5. Getting Started: Simple configuration and deployment process

Next Steps

In the next modules, we’ll dive deeper into:

• Hands-on deployment of a medium-sized LLMs,

• Advanced configurations and optimizations (tool calling, LoRA, structured outputs…)

Resources

• Ray Serve LLM with Anyscale Documentation

• Deploy LLM templates

• Ray Serve LLM Documentation

• vLLM Documentation

Ready to start serving LLMs with Ray? Let’s move on to the next module!