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DockerModelRunner
Benchmarking LLMs with Docker Model Runner: A Complete Performance Guide
Benchmarking LLMs: A Comprehensive Performance Guide Introduction In the rapidly evolving landscape of AI development, understanding how your models perform locally...
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Table of Contents
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Benchmarking LLMs A Comprehensive Performance Guide
Introduction
What You’ll Learn
Prerequisites
Understanding Performance Metrics
1. Time to First Token (TTFT)
2. Time Per Output Token (TPOT)
3. Throughput
Part 1 Setting Up Docker Model Runner
Enable Docker Model Runner
Choose Your Model
Part 2 Setting Up the Benchmark Environment
Install Python Dependencies
Create the Benchmark Script
Part 3 Running Your First Benchmark
Step 1 Start the Model
Step 2 Run the Benchmark
Expected Output
Part 4 Understanding Your Results
Interpreting TTFT (Time to First Token)
Interpreting TPOT (Time Per Output Token)
Interpreting Throughput
Part 5 Advanced Benchmarking Scenarios
Scenario 1 Comparing Quantization Levels
Scenario 2 Stress Testing with High Concurrency
Scenario 3 Different Context Lengths
Part 6 Optimizing Performance
Hardware Optimization
Model Selection Guidelines
Configuration Tuning
Part 7 Real-World Application Patterns
Pattern 1 RAG (Retrieval-Augmented Generation)
Pattern 2 Code Generation
Pattern 3 Chatbot / Conversational AI
Part 8 Monitoring and Observability
Creating a Performance Dashboard
System Resource Monitoring
Part 9 Troubleshooting Common Issues
Issue 1 Slow TTFT (> 2 seconds)
Issue 2 Low Throughput (< 5 tokens/sec)
Issue 3 Model Runner Connection Errors
Issue 4 Out of Memory Errors
Part 10 Production Deployment Checklist
Performance Requirements
Benchmark Validation
Monitoring Setup
Conclusion
Next Steps
Additional Resources
→
Index
