vllm-tuner

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An intelligent tuner for vLLM that automatically monitors GPU metrics, uses Bayesian optimization to tune parameters

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# vLLM-Tuner

An intelligent tuner for vLLM that automatically monitors GPU metrics, uses Bayesian optimization to tune parameters (`batch_size`, `max_num_batched_tokens`, `max_num_seqs`, `gpu_memory_utilization`) to maximize throughput while minimizing latency and balancing memory, respecting user-provided constraints.

## Features

- **Intelligent Profiling**: Monitor GPU memory, utilization, and vLLM metrics automatically
- **Adaptive Parameter Search**: Bayesian optimization (Optuna) with multi-objective support (throughput, latency, memory)
- **vLLM-Aware Integration**: Parse vLLM logs for KV cache utilization, preemption tracking, and guidance
- **Multi-GPU Support**: Handle data-parallel and model-parallel (tensor/pipeline) configurations
- **User-Friendly Configuration**: Simple YAML configs to specify objectives and constraints
- **Rich Reporting**: Plotly interactive HTML reports with trial progression, Pareto front, and GPU telemetry
- **Extensibility**: Custom workloads and plugins for specific deployment scenarios

## Installation

```bash
# Create and activate uv environment
uv venv --seed --python 3.10
source .venv/bin/activate

# Install vllm-tuner
uv pip install git+https://github.com/jranaraki/vllm-tuner

# Install vLLM
uv pip install vllm --torch-backend=auto
```

## Configuration

Configuration is done via YAML file, see [default.yaml](config/default.yaml), and here are the key settings:

### Multi-Objective Weights (must sum to 100)
```yaml
objectives:
  throughput: 60  # Weight for throughput maximization
  latency: 30     # Weight for latency minimization
  memory: 10      # Weight for memory efficiency
```

### Search Space
```yaml
search_space:
  batch_size: [1, 256]  # Range or override defaults
  gpu_memory_utilization: [0.6, 0.99]
  tensor_parallel_size: [1, 2, 4]
```

### Workload
```yaml
workload:
  dataset_name: "tatsu-lab/alpaca"  # HF dataset
  sample_size: 100                  # Number of prompts
  concurrent_requests: 10           # Concurrent clients
```

Store the config file under `configs` folder.

## Run

### Basic Tuning
```bash
# Run tuning study
vllm-tuner tune --config config/default.yaml --study-name my_study
```

## Output Structure

Studies and reports are saved to `studies/<study_name>/` and `reports`, respectively:

```text
├── configs
│		 └── default.yaml                   # vLLM-Tuner config
├── reports
│		 └── my_study
│		     └── report.html                # Interactive Plotly report
└── studies
    └── my_study
        ├── baseline                        # Baseline metrics (if enabled)
        │		 ├── baseline_config.yaml
        │		 ├── baseline_metrics.json
        │		 ├── baseline_summary.txt
        │		 └── logs
        │		     └── vllm_baseline.log
        ├── configs                         # Summary & best configs
        │		 ├── best_config.json
        │		 ├── best_config.yaml
        │		 ├── summary.json
        │		 └── trials.json
        ├── logs                            # vLLM server logs
        │		 ├── vllm_trial_0.log
        │		 ├── vllm_trial_1.log
        │		 ├── vllm_trial_2.log
        │		 ├── vllm_trial_3.log
        │        ├── vllm_trial_4.log
        │		 └── ...
        └── optuna.db                       # SQLite study database

```

And the final report is as follows:

![report_screenshot.png](https://raw.githubusercontent.com/jranaraki/vllm-tuner/main/docs/report_screenshot.png)

## Documentation

For detailed information, see the [comprehensive documentation](docs/README.md).

## Citing

If you find vllm-tuner useful and interested in citing this work, please use the following BibTex entry:

```
@software{vllmtuner2026,
  author = {Javad Anaraki},
  title = {vllm-tuner: Automated Parameter Tuning for vLLM via Bayesian Optimization},
  url = {https://github.com/jranaraki/vllm-tuner},
  version = {0.1.0},
  year = {2026},
}
```

## Acknowledgments

- [Optuna](https://optuna.org/) for Bayesian optimization
- [vLLM](https://github.com/vllm-project/vllm) for high-performance serving
- [Hugging Face Datasets](https://huggingface.co/docs/datasets/) for workloads

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