A Comprehensive Guide to NIM LLM Latency-Throughput Benchmarking

Benchmarking Guide

• Overview
  • Executive Summary
  • Introduction to LLM Inference Benchmarking
  • Background On How LLM Inference Works
• Metrics
  • Time to First Token (TTFT)
  • End-to-End Request Latency (e2e_latency)
  • Inter-token Latency (ITL)
  • Tokens Per Second (TPS)
  • Requests Per Second (RPS)
• Parameters and Best Practices
  • Use Cases
  • Load Control
  • Other Parameters
• Using AIPerf to Benchmark
  • Step 1. Setting Up an OpenAI-Compatible LLama-3 Inference Service with NVIDIA NIM
  • Step 2. Setting Up AIPerf and Warming Up: Benchmarking a Single Use Case
  • Step 3. Sweeping through a Number of Use Cases
  • Step 4. Analyzing the Output
  • Step 5. Interpreting the Results
• Benchmarking LoRA Models
  • Best practices for Multi-LoRA deployment Performance Benchmarking

Performance

• Benchmarks
  • Llama-3.3-70b-instruct Results
    • Version: 1.8.0
    • Version: 1.5.0
  • Llama-3.1-8b-instruct Results
    • Version: 1.8.0
    • Version: 1.3.0
  • Llama-3.1-70b-instruct Results
    • Version: 1.3.0
  • Hardware Specifications
    • NVIDIA H100
    • NVIDIA H200
    • NVIDIA A100
    • NVIDIA L40s