Overview
Description
vLLM is the fastest and most cost-effective way to serve large language models at scale. It slashes inference costs through state-of-the-art throughput and smarter memory management with PagedAttention. The bottom line: deploy any LLM in production with lower latency and lower infrastructure bills.
Technical scorecard
License
Apache-2.0
Commercial use
Yes
OpenAI-compatible API
Yes
REST API
Yes
Fine-tuning support
No
Quantization support
Yes
Docker available
Yes
GUI / no-code available
No
Telemetry
Unknown
Offline after setup
Yes
Data & Privacy
Does it send data online?
After setup, this listing is marked as usable offline. Confirm network behavior against the upstream project before regulated deployment.
Does it store history?
Not verified in this directory yet. Review the upstream docs for persistence, logs, and workspace storage.
License checks?
Commercial use is marked as allowed or likely allowed by the listed license.
Telemetry?
Unknown
Last verified: Jul 22, 2026. Maintainer verification should be treated as directory guidance, not legal advice.
Deploying vLLM in production?
Stop silent telemetry leaks before they trigger an unintended data leak. Get our Airgap Certainty Blueprint for vLLM.
Request Privacy and Telemetry AuditSetup & Installation
A developer can usually get this running with standard docs.
# Start with the official project repository
# https://github.com/vllm-project/vllmHardware Requirements
Works Well With