Quickstart

This guide will help you get up and running with Inference Engine Arena quickly. You’ll learn how to install the framework, start an inference engine, and run a simple benchmark.

Installation

It’s recommended to use uv, a very fast Python environment manager, install it from here or curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/Inference-Engine-Arena/inference-engine-arena.git
cd inference-engine-arena

# Install dependencies
uv venv myenv
source myenv/bin/activate
uv pip install -e .

If you encounter any issues, please refer to the Troubleshooting guide or submit an issue on GitHub. After installing the framework, you can start the inference engines and run benchmarks with two main approaches:

Manual mode: Starting engines and running benchmarks separately, designed for benchmarking simple test cases and experimental runs.
Batch mode: Benchmark process initiated by ~~a single command or~~ a single YAML file, suitable for complex and large-scale test case benchmarks, or sharing reproducible experiments with others.

Manual Mode

In manual mode, you start engines first, then run benchmarks. We’ll show how to benchmark NousResearch/Meta-Llama-3.1-8B using vLLM.

Starting an Engine

Start a vLLM engine with NousResearch/Meta-Llama-3.1-8B. You can add any vLLM parameters that are compatible with vllm serve after vllm.Optional: You can also set environment variables as you need.

# export VLLM_USE_V1=1
# export HUGGING_FACE_HUB_TOKEN="YOUR_HUGGING_FACE_TOKEN"
# export CUDA_VISIBLE_DEVICES=1
arena start vllm NousResearch/Meta-Llama-3.1-8B --dtype bfloat16 --enable-prefix-caching

You’ll see logs as the engine starts up. Wait until you see:

[INFO] src.engines.engine_manager: Started engine vllm
[INFO] src.cli.commands: Engine started successfully: vllm

The process will running behind the scene. And you can check the status of running engines:

arena list

You can see the engine currently running.

Inference Engines (1):

Name            Container ID    Model                     Status     Endpoint                       GPU Info
--------------------------------------------------------------------------------------------------------------
vllm            34547e9b5aa8    NousResearch/Meta-Llama-3 running    http://localhost:8000          NVIDIA H100 80GB HBM3

Running Benchmarks on the Engine

Once your engine is running, you can run a simple benchmark:

# Run a conversational benchmark on vLLM
arena run --engine vllm --benchmark conversational_short conversational_medium summarization

Tips: You may run multiple benchmarks on the same engine. Or multiple benchmarks on different engines at the same time. Refer to Run Benchmarks for more details.Expected output will show metrics like throughput, ttft, etc. The results will be saved to ./results by default. Refer to the Dashboard and Leaderboard for more details.

Running benchmark: conversational_short on vllm
······
src.benchmarks.benchmark_runner: Benchmark completed successfully. Key metrics: input_throughput=975.0938019114691, output_throughput=899.8165604039037, ttft=19.5751617802307
······
Benchmark Run Summary:
  Run ID: run-20250420-231844-0e03bab4
  Start time: 2025-04-20T23:18:44.583338
  End time: 2025-04-20T23:23:17.441123
  Duration: 272.9 seconds
  Engines: vllm
  Benchmark types: conversational_short, conversational_medium, conversational_long, rewrite_essay, summarization, write_essay
  Sub-runs: 6

Results saved to: ./results/run-20250420-231844-0e03bab4

Stopping an Engine

If you don’t need this engine anymore, or wish to adjust its parameters:

arena stop vllm

The output below confirms the engine has been stopped successfully:

2025-04-20 23:36:03,357 [INFO] src.cli.commands: Stopped engine: vllm

Batch Mode

For more complex scenarios, where you need to benchmark multiple engines with different configurations and different benchmark configurations,you can define your experiments in a YAML file and run them with a single command. Here, we use /example_yaml/Meta-Llama-3.1-8B-varied-max-num-seq.yaml as an example, which benchmarks the same benchmark type with different max-num-seqs configurations. Tips: You may also refer to other examples in the /example_yaml directory. And runyaml section for more details.

Example YAML Configuration

Take a look at the YAML file:

runs:
  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 1"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 2"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 4"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 8"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 16"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 32"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

  - engine:
      - type: vllm
        model: NousResearch/Meta-Llama-3.1-8B
        args: "--max-num-batched-tokens 163840 --max-num-seqs 256"
        env: {}
    benchmarks:
      - type: conversational_short
        dataset-name: random
        random-input-len: 100
        random-output-len: 100
        random-prefix-len: 0
        num-prompts: 50
        request-rate: 10

If you want to test more engines or benchmark types, you can continue adding engine with different configurations and benchmark configurations afterward.Run the benchmark configuration:

arena runyaml example_yaml/Meta-Llama-3.1-8B-varied-max-num-seq.yaml

This will automatically run the all experiments defined in the YAML file.

Viewing Results

After running benchmarks, there are two ways to view and analyze the results.

Dashboard

Here you can visualize and compare results from a single benchmark run, and share them with our community.

arena dashboard

For a detailed introduction to the dashboard, see Dashboard

Leaderboard

Here you can compare your results across different benchmark runs, and can visit the community for further results.

arena leaderboard

For a detailed introduction to the leaderboard, see Leaderboard

Upload Results

To share your benchmark results with the community, use these commands:

# Upload signle results to the global leaderboard
arena upload sub-run-20250420-211509-vllm-Meta-Llama-3-1-8B-conversational-short-582ae937.json
# Upload all results to the global leaderboard
arena upload
# Anonymous data upload
arena upload --no-login

If you don’t use the --no-login flag, you’ll need to log in to authorize the upload. We recommend starting with a single JSON file upload to complete the login process, then using the command to upload all your data. Alternatively, you can first share your results in the dashboard using the “Share Subrun to Global Leaderboard” button. Don’t worry about duplicate submissions - our system automatically deduplicates any repeated data.

Getting Started

Usage

Roadmap

Installation

Manual Mode

Batch Mode

Viewing Results

Dashboard

Leaderboard

Upload Results

Getting Started

Usage

Roadmap

​Installation

​Manual Mode

​Batch Mode

​Viewing Results

Dashboard

Leaderboard

​Upload Results

Installation

Manual Mode

Batch Mode

Viewing Results

Upload Results