LaunchpadHQ

vLLM

by Open Source (UC Berkeley)

Overview

High-throughput LLM serving engine

Best for: High-performance LLM serving; production inference; PagedAttention for throughput; batch processing

At a glance

Pricing

Free

Difficulty
Advanced
Time to productivity
2 hours
Privacy
High
Learning curve
Steep

Ideal for

ML engineersplatform teamscompanies serving LLMs at scaleGPU server operators

Key capabilities

Works with

  • Text
  • embeddings

Outputs

  • OpenAI-compatible API responses
  • batch outputs

Mobile access

How to use vLLM on phones and tablets.

  • Mobile web: Works in a mobile browser (responsive or dedicated mobile site).

Free Tier

Completely free and open-source (Apache 2.0)

Limits: None (hardware-limited; designed for GPU servers)

When to upgrade: N/A (fully free)

Technical Details

Type: local
Offline: Yes
API: Yes
Languages: Multilingual (depends on model)
Integrations: OpenAI-compatible API, Hugging Face models, Ray (distributed), Kubernetes, LangChain, major frameworks

Alternatives

OpenAI API-compatible local inference

FreeCompletely free to use with no credit card required.AdvancedLocal & Open-Source AIWeb

Free: Completely free and open-source (MIT)