LLM Benchmark Suite

This repository contains a comprehensive suite of benchmarks for evaluating LLM serving systems. The suite includes multiple scenarios to test different aspects of model performance.

The workload simulated in these benchmarks is a multi-round QA (question answering) task with multiple users interacting with an LLM engine concurrently. An illustration is shown below:

Available Benchmarks

1. ShareGPT Benchmark
2. Replays real-world conversations from ShareGPT

3. Default QPS: 1.34

4. Short Input, Short Output (Synthetic)

5. System prompt: 0 tokens
6. Chat history: 256 tokens
7. Answer length: 20 tokens

8. Default QPS: 15

9. Long Input, Short Output (Synthetic)

10. System prompt: 1000 tokens
11. Chat history: 20000 tokens
12. Answer length: 100 tokens
13. Default QPS: 0.1

Running Benchmarks

The unified script run_benchmarks.sh can run any combination of benchmarks with consistent configuration:

# Run all benchmarks with default QPS
./run_benchmarks.sh <model> <base_url> <save_file_key> all

# Run specific benchmarks with default QPS
./run_benchmarks.sh <model> <base_url> <save_file_key> sharegpt short-input

# Run specific benchmarks with custom QPS
./run_benchmarks.sh <model> <base_url> <save_file_key> sharegpt short-input 1.34 2.0 3.0

Examples

# Run all benchmarks with default QPS
./run_benchmarks.sh meta-llama/Llama-3.1-8B-Instruct http://localhost:8000 /mnt/requests/benchmark all

# Run ShareGPT and short input benchmarks with custom QPS
./run_benchmarks.sh meta-llama/Llama-3.1-8B-Instruct http://localhost:8000 /mnt/requests/benchmark sharegpt short-input 1.34 2.0 3.0

Output Files

Results are saved in CSV format with the following naming convention: - ShareGPT: <save_file_key>_sharegpt_output_<qps>.csv - Short Input: <save_file_key>_short_input_output_<qps>.csv - Long Input: <save_file_key>_long_input_output_<qps>.csv

Processing Results

Time To First Token (TTFT)

python3 synthetic-multi-round-qa/multi-round-qa.py --process-summary <your_csv_file>

Inter-Token Latency (ITL)

python3 synthetic-multi-round-qa/calculat_itl.py

Notes

• The warm-up phase is automatically handled for all benchmarks
• All scripts handle their paths correctly regardless of where they're run from
• QPS values can be customized through command-line arguments
• Results are saved in CSV format with the QPS value in the filename

Benchmark Docker and Kubernetes Setup

This directory contains the necessary files to run the benchmark in Docker and Kubernetes environments.

Files

• Dockerfile: Defines the Docker image for running the benchmark
• benchmark-job.yaml: Kubernetes job configuration
• run_benchmarks.sh: Main benchmark script

Environment Variables

The following environment variables can be configured:

• MODEL: The model name to benchmark (default: "meta-llama/Llama-3.1-8B-Instruct")
• BASE_URL: The base URL of the vLLM server (default: "http://localhost:8000")
• SAVE_FILE_KEY: Prefix for the output files (default: "benchmark_results")
• SCENARIOS: Benchmark scenarios to run (default: "all")
• Options: "all", "sharegpt", "short-input", "long-input"
• QPS_VALUES: Space-separated list of QPS values to test (default: "1.34")

Pulling a pre-built Docker Image

docker pull lmcache/lmcache-benchmark

Building your own Docker Image

docker build -t your-registry/benchmark:latest .

Running in Docker

docker run -e MODEL="meta-llama/Llama-3.1-8B-Instruct" \
           -e BASE_URL="http://vllm-service:8000" \
           -e SAVE_FILE_KEY="benchmark_results" \
           -e SCENARIOS="all" \
           -e QPS_VALUES="1.34 2.0 3.0" \
           -v /path/to/results:/app/results \
           your-registry/benchmark:latest

# To use the pre-built image, replace <your-registry/benchmark:latest> with <lmcache/lmcache-benchmark>

Running in Kubernetes

1. Create a PersistentVolumeClaim for storing results:

kubectl apply -f benchmark-results-pvc.yaml

1. Deploy the benchmark job:

kubectl apply -f benchmark-job.yaml

1. Monitor the job:

kubectl get jobs
kubectl logs job/benchmark-job

Output

The benchmark results will be saved in the mounted volume with the following structure: - {SAVE_FILE_KEY}_sharegpt_qps{X}.csv for ShareGPT benchmarks - {SAVE_FILE_KEY}_short_input_qps{X}.csv for short input benchmarks - {SAVE_FILE_KEY}_long_input_qps{X}.csv for long input benchmarks

Where X is the QPS value used for that run.

Reproduce our results

To reproduce results from our latest benchmarking runs against other open-source LLM serving systems, please refer to the configuration scripts inside the folders in the configs directory.

Latest results:

• April 2025