Summary & Outlook

Summary & Outlook#

Congratulations! You’ve successfully learned how to deploy a medium-sized LLM with Ray Serve LLM. Let’s summarize what we’ve covered and look ahead to other possibilities.

What We Accomplished#

Module 2 Summary:

  1. Overview: Understood why medium-sized models (70B parameters) are ideal for production workloads

  2. Configuration: Set up Ray Serve LLM with tensor parallelism across 8 GPUs

  3. Local Deployment: Deployed locally and tested with various inference scenarios

  4. Anyscale Services: Deployed to production with zero code changes

  5. Advanced Topics: Enabled monitoring, optimized concurrency

Key Takeaways#

  • No Code Changes: Same configuration works locally and in production

  • Tensor Parallelism: Essential for medium models to distribute across multiple GPUs

  • Production Ready: Anyscale Services provide enterprise-grade deployment

  • Monitoring: Comprehensive dashboards for performance optimization

  • Scalability: Multiple optimization strategies for different use cases

How Other Sizes Differ#

Now that you’ve seen a medium model deployment, here’s how other sizes would differ:

Small Models (7B-13B):

  • No tensor parallelism needed

  • Single GPU deployment

  • Faster startup time

  • Lower concurrency but simpler setup

Large Models (400B+):

  • Pipeline parallelism across multiple nodes

  • More complex infrastructure requirements

  • Higher costs but maximum capability

  • Research and specialized use cases

Next Steps#

Ready to explore more? Consider:

  1. Try different model sizes - Deploy small or large models

  2. Experiment with optimizations - Test quantization and concurrency tuning

  3. Build applications - Create end-to-end AI applications

  4. Explore advanced features - Multi-model deployments, custom endpoints

Resources#

You now have the knowledge to deploy medium-sized LLMs in production with Ray Serve LLM!

Contents