TGI vs 
LMDeploy
TGI vs LMDeploy compared for 2026 — features, license, ease of use, performance and which one to choose. Hugging Face's production text server vs Toolkit for compressing and serving LLMs.
Updated regularly · curated by olud.ai
TGI vs LMDeploy at a glance
| Spec | TGI | LMDeploy |
|---|---|---|
| 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 | Rust | Python |
| Ease of use | Advanced | Advanced |
| Best for | teams in the Hugging Face ecosystem | teams optimizing quantized serving |
| GitHub stars | — | 8k |
Feature comparison
| Feature | TGI | LMDeploy |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✓ | ✗ |
| Docker | ✓ | ✓ |
How TGI and LMDeploy score
| Criterion | TGI | LMDeploy |
|---|---|---|
| Popularity | n/a | 2.5 |
| Maintenance | n/a | 5.0 |
| 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
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
LMDeploy
LMDeploy is a toolkit for compressing, quantizing and serving LLMs with high request throughput via its TurboMind engine.
- High throughput via the TurboMind engine
- Built-in quantization and compression
- Efficient KV-cache management
Key differences
TGI is inference server, while LMDeploy is inference server. In short, TGI fits teams in the Hugging Face ecosystem, and LMDeploy fits teams optimizing quantized serving.
Which should you choose?
Choose TGI for teams in the Hugging Face ecosystem. Choose LMDeploy for teams optimizing quantized serving.
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 TGI or LMDeploy easier to use?
Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.
Are TGI and LMDeploy free?
TGI is free and open source (Apache-2.0), and LMDeploy is free and open source (Apache-2.0). Neither charges for the core software.
Can I run TGI and LMDeploy locally?
TGI: self-hosted · LMDeploy: self-hosted. Both can be used without sending your data to a third-party cloud where their setup allows.
TGI vs LMDeploy — which should I pick in 2026?
Choose TGI for teams in the Hugging Face ecosystem. Choose LMDeploy for teams optimizing quantized serving.
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