Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

vLLM Optimizer

Find better vLLM serving settings for your workload. Define a search space in YAML; vllm-opt runs the benchmarks and reports the best observed settings.

Run your first experiment · Documentation · PyPI

This is independent community alpha software, not affiliated with vLLM. Execution needs Python 3.11–3.12, Linux/WSL, an NVIDIA GPU, and a local model. Native Linux, L40/H100, tensor-parallel, and multi-GPU evidence remain hardware validation pending. See the compatibility matrix.

Install

Use Command
Run GPU experiments on Linux/WSL pip install "vllm-optimizer[runtime]"
Inspect configs and saved results on any supported OS pip install vllm-optimizer

Use a virtual environment. The runtime extra installs vLLM and GuideLLM; the core package requires an existing runtime to execute experiments. See installation choices for setup and existing CUDA environments.

First experiment

The quick start contains one complete example:

  1. Install the runtime in a virtual environment.
  2. Copy the YAML and set your local model path. Models are not downloaded.
  3. Validate and run it; open the printed report.html path.

Read the walkthrough in 5–10 minutes. Installation and benchmark time depend on your machine and model.

What you can tune

  • Fixed or tunable vLLM flags and environment variables.
  • Grid, Random, or TPE search with a baseline comparison.
  • GuideLLM or native vLLM Bench Serve workloads.
  • Throughput objectives subject to latency/throughput targets, such as p99 TTFT ≤ "200 ms" or request throughput ≥ "50 req/s".

Search keeps its configured budget after meeting a target. Only configurations satisfying every limit can win. See search and scoring.

See a real report

Open the interactive RTX 3080 report · Experiment YAML · Baseline-control example

Report showing throughput, confidence, settings, and all trials

This deliberately small workload measured 194.34 → 1,082.27 output tokens/s. It demonstrates reporting and latency trade-offs; it is not a production benchmark or an expected improvement for other workloads. The published example used vLLM 0.28.0 and GuideLLM 0.7.3; dated runtime details and required WSL flags are in compatibility.

Next steps

Task Guide
Set fixed values and search ranges Configuration
Choose workloads and datasets Benchmarking
Use native vLLM benchmarking vLLM Bench Serve example
Understand confidence and target eligibility Reports
Retry, reproduce, or regenerate results CLI reference
Run independent trials on separate GPUs Parallel trials
Resolve runtime failures Troubleshooting
Find every YAML section Complete YAML

Use vllm-opt --config experiment.yaml --verbose to stream logs. Full trial logs are saved regardless of verbosity; NO_COLOR disables terminal colors.

Install vllm-optimizer, import vllm_optimizer, and run vllm-opt. The former vtune aliases are deprecated; see migration.

Contribute

Contributing · Architecture · Roadmap · Release notes

Metadata

Release files for vllm-optimizer 0.1.0a21

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for vllm-optimizer 0.1.0a21
File Size Uploaded Provenance
vllm_optimizer-0.1.0a21.tar.gz 139.7 kB With provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for vllm-optimizer 0.1.0a21
File Interpreter ABI Platform
vllm_optimizer-0.1.0a21-py3-none-any.whl Python 3 none any Details

Total release size: 307.9 kB

Release files / vllm_optimizer-0.1.0a21.tar.gz

Download URL vllm_optimizer-0.1.0a21.tar.gz
Size 139.7 kB
Tags Source
SHA-256 checksum
How to use checksums
4d1d6dd3f162d19c4e829909955dbd37336ab2a1583f1b27954f6858bee6dd48
BLAKE2b-256 checksum
How to use checksums
60316f198948d6017572fe094c705c1db22725e631641de1573d9579ef1eb96d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 16, 2026.

Transparency log

Release files / vllm_optimizer-0.1.0a21-py3-none-any.whl

Download URL vllm_optimizer-0.1.0a21-py3-none-any.whl
Size 168.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
41ec70155469be2f42cc47da1514a0bcb6ca04c49d33da920c7bd7feb1a34613
BLAKE2b-256 checksum
How to use checksums
646368f08284b0f3fb93beb3c09de13865cc2925d877f99980e0d3013024c4e2
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 16, 2026.

Transparency log

Security for vllm-optimizer 0.1.0a21

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Full provenance

All 2 files in this release have been uploaded with provenance metadata.

Select from 2 files

Attestation for vllm_optimizer-0.1.0a21.tar.gz

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Oct 16, 2026.

Transparency log

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page