Module 3: vLLM Performance Optimization

Introduction

With your LLM deployed and benchmarked, it’s time to optimize performance for real-world production workloads. This module focuses on maximizing inference efficiency using vLLM tuning techniques that can dramatically improve latency and throughput without requiring additional hardware.

Performance optimization is critical because:

  • User Experience: Sub-second response times are essential for interactive applications like chatbots and coding assistants

  • Cost Efficiency: Better performance means serving more users with the same infrastructure, directly reducing per-request costs

  • Scalability: Optimized models can handle higher concurrent loads without degradation

  • Resource Utilization: Proper tuning maximizes GPU utilization and minimizes memory waste

Learning Objectives

By the end of this module, you will be able to:

  • Understand key vLLM performance parameters and their impact on latency/throughput

  • Apply systematic optimization techniques to reduce Time To First Token (TTFT)

  • Configure memory management and batching for optimal resource utilization

  • Implement performance tuning strategies for chat applications with concurrent users

  • Measure and validate optimization improvements using real-world benchmarks

What You’ll Learn

Performance Fundamentals

  • Latency vs Throughput: Understanding the trade-offs and when to optimize for each

  • Time To First Token (TTFT): The critical metric for interactive user experience

  • Memory Management: KV cache optimization and efficient GPU memory utilization

  • Batching Strategies: Dynamic batching and continuous batching for concurrent requests

vLLM Configuration Mastery

  • Engine Parameters: max_model_len, max_num_seqs, block_size and their performance impact

  • Attention Mechanisms: PagedAttention configuration for memory efficiency

  • Scheduling: Request scheduling and queue management optimization

  • GPU Utilization: Maximizing hardware efficiency through proper configuration

Real-World Case Study

You’ll work through a practical scenario: optimizing granite-3.3-8b-instruct for a chat application serving 32 concurrent users with sub-2048 token responses. This hands-on experience mirrors real production optimization challenges.

Module Structure

3.1 Performance Tuning Practice

Hands-on optimization of granite-3.3-8b-instruct with systematic parameter tuning, performance measurement, and iterative improvement to achieve optimal latency for chat workloads.

3.2 Optimization Conclusion

Review optimization results, best practices summary, and guidelines for applying these techniques to different models and use cases in production environments.

Prerequisites

Before starting this module, ensure you have:

  • Completed Module 2 (Evaluation)

  • Access to an OpenShift cluster with GPU resources

  • Basic understanding of inference serving concepts

  • GuideLLM benchmarking experience from Module 2

Success Metrics

By module completion, you should achieve:

  • Measurable TTFT improvement: Reduce time to first token by 20-50%

  • Increased throughput: Handle more concurrent requests with the same hardware

  • Optimized resource usage: Achieve >80% GPU utilization during peak loads

  • Production readiness: Understand how to apply these techniques to your specific use cases

Let’s begin optimizing your LLM inference performance!