vLLM vs 
TGI
vLLM vs TGI compared for 2026 — features, license, ease of use, performance and which one to choose. High-throughput serving for production vs Hugging Face's production text server.
Updated regularly · curated by OpenSourceAI.tech
vLLM vs TGI at a glance
| Spec | vLLM | TGI |
|---|---|---|
| Category | Inference server | Inference server |
| Type | Inference server | Inference server |
| License | Apache-2.0 | Apache-2.0 |
| Runs locally | Self-hosted | Self-hosted |
| Primary language | Python | Rust |
| Ease of use | Advanced | Advanced |
| Best for | production teams serving models at scale | teams in the Hugging Face ecosystem |
| GitHub stars | 87.6k | — |
Feature comparison
| Feature | vLLM | TGI |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✓ | ✓ |
| Docker | ✓ | ✓ |
How vLLM and TGI score
| Criterion | vLLM | TGI |
|---|---|---|
| Popularity | 4.5 | n/a |
| Maintenance | 5.0 | n/a |
| Ease of use | 2.5 | 2.5 |
| Privacy | 4.5 | 4.5 |
| License freedom | 5.0 | 5.0 |
Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.
What each one is
vLLM
vLLM is a high-throughput inference and serving engine using PagedAttention to maximize GPU utilization, the default choice for serving open models at scale.
- Best-in-class throughput via PagedAttention
- OpenAI-compatible server, broad model support
- The de-facto standard for production serving
TGI
Text Generation Inference (TGI) is Hugging Face's production-grade server for deploying and serving LLMs, with continuous batching, quantization and tight Hub integration.
- Production-grade, battle-tested at Hugging Face
- Continuous batching and quantization built in
- Tight integration with the HF Hub
Key differences
vLLM is inference server, while TGI is inference server. In short, vLLM fits production teams serving models at scale, and TGI fits teams in the Hugging Face ecosystem.
Which should you choose?
Choose vLLM for production teams serving models at scale. Choose TGI for teams in the Hugging Face ecosystem.
There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.
Frequently asked questions
Is vLLM or TGI easier to use?
Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.
Are vLLM and TGI free?
vLLM is free and open source (Apache-2.0), and TGI is free and open source (Apache-2.0). Neither charges for the core software.
Can I run vLLM and TGI locally?
vLLM: self-hosted · TGI: self-hosted. Both can be used without sending your data to a third-party cloud where their setup allows.
vLLM vs TGI — which should I pick in 2026?
Choose vLLM for production teams serving models at scale. Choose TGI for teams in the Hugging Face ecosystem.
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