Module 3 Conclusion: vLLM Optimization Mastery

What You’ve Accomplished

You’ve mastered vLLM performance optimization techniques through hands-on granite-3.3-8b-instruct tuning, gaining practical experience with parameters that directly impact production performance.

Production Application

Optimization Methodology

  1. Baseline measurement → 2. Single parameter changes → 3. Load testing → 4. Monitoring

Parameter Selection by Use Case

  • Latency-critical: Prioritize TTFT, smaller batches

  • Throughput-focused: Maximize concurrent requests, optimize batching

  • Cost-optimized: Maximize GPU utilization, intelligent scaling

Common Client Scenarios

  • High latency (TTFT >500ms): Memory bandwidth optimization → 40-60% improvement

  • Low throughput: Batching optimization → 50-100% increase

  • Resource waste (<60% GPU): Parameter rebalancing → 20-40% cost reduction

Business Value Framework

Cost Impact: Cost Savings = (Original GPU Hours - Optimized GPU Hours) × Hourly Rate

Client Engagement:

  • Discovery: Identify bottlenecks, quantify opportunities

  • PoC: Demonstrate improvements, establish baselines

  • Production: Apply methodology, implement monitoring

Integration with Module 4

Your optimization foundation enables quantization success:

  • Performance baseline for measuring quantization impact

  • Memory management skills essential for compressed models

  • Throughput optimization compounds with quantization gains

Key Takeaway

The systematic approach developed here - measure, optimize, validate, monitor - applies across all LLM deployment aspects and prepares you for Module 4’s advanced model compression techniques.

Ready for quantization? Your optimized vLLM foundation will amplify the transformative impact of model compression.