vLLM vs 
LMDeploy
vLLM vs LMDeploy compared for 2026 — features, license, ease of use, performance and which one to choose. High-throughput serving for production vs Toolkit for compressing and serving LLMs.
Updated regularly · curated by olud.ai
vLLM vs LMDeploy at a glance
| Spec | vLLM | 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 | Python | Python |
| Ease of use | Advanced | Advanced |
| Best for | production teams serving models at scale | teams optimizing quantized serving |
| GitHub stars | 86.8k | 8k |
Feature comparison
| Feature | vLLM | LMDeploy |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✓ | ✗ |
| Docker | ✓ | ✓ |
How vLLM and LMDeploy score
| Criterion | vLLM | LMDeploy |
|---|---|---|
| Popularity | 4.5 | 2.5 |
| Maintenance | 5.0 | 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
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
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
vLLM is inference server, while LMDeploy is inference server. In short, vLLM fits production teams serving models at scale, and LMDeploy fits teams optimizing quantized serving.
Which should you choose?
Choose vLLM for production teams serving models at scale. 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 vLLM or LMDeploy easier to use?
Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.
Are vLLM and LMDeploy free?
vLLM 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 vLLM and LMDeploy locally?
vLLM: self-hosted · LMDeploy: self-hosted. Both can be used without sending your data to a third-party cloud where their setup allows.
vLLM vs LMDeploy — which should I pick in 2026?
Choose vLLM for production teams serving models at scale. Choose LMDeploy for teams optimizing quantized serving.
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