LMDeploy vs 
OpenLLM
LMDeploy vs OpenLLM compared for 2026 — features, license, ease of use, performance and which one to choose. Toolkit for compressing and serving LLMs vs Serve any open model as an OpenAI API in one command.
Updated regularly · curated by OpenSourceAI.tech
LMDeploy vs OpenLLM at a glance
| Spec | LMDeploy | OpenLLM |
|---|---|---|
| Category | Inference server | Inference server |
| Type | Inference server | Serving framework |
| License | Apache-2.0 | Apache-2.0 |
| Runs locally | Self-hosted | Yes |
| Primary language | Python | Python |
| Ease of use | Advanced | Beginner |
| Best for | teams optimizing quantized serving | going from model name to production endpoint fast |
| GitHub stars | 8k | 12.4k |
Feature comparison
| Feature | LMDeploy | OpenLLM |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✗ | ✗ |
| Docker | ✓ | ✓ |
How LMDeploy and OpenLLM score
| Criterion | LMDeploy | OpenLLM |
|---|---|---|
| Popularity | 2.5 | 3.0 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 2.5 | 5.0 |
| Privacy | 4.5 | 5.0 |
| 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
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
OpenLLM
OpenLLM by BentoML runs open models behind an OpenAI-compatible endpoint with one command, adds a chat UI, and packages everything for Docker or cloud deployment.
- One command from model to OpenAI-compatible API
- Built-in chat UI for quick testing
- Clean path to Docker and cloud deployment via BentoML
Key differences
LMDeploy is inference server, while OpenLLM is serving framework. LMDeploy leans more advanced-friendly, whereas OpenLLM is more suited to beginner users. They also differ in how they run (Self-hosted vs Yes). In short, LMDeploy fits teams optimizing quantized serving, and OpenLLM fits going from model name to production endpoint fast.
Which should you choose?
Choose LMDeploy for teams optimizing quantized serving. Choose OpenLLM for going from model name to production endpoint fast.
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 LMDeploy or OpenLLM easier to use?
OpenLLM is generally the easier of the two to get started with, while LMDeploy rewards more setup with more control.
Are LMDeploy and OpenLLM free?
LMDeploy is free and open source (Apache-2.0), and OpenLLM is free and open source (Apache-2.0). Neither charges for the core software.
Can I run LMDeploy and OpenLLM locally?
LMDeploy: self-hosted · OpenLLM: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LMDeploy vs OpenLLM — which should I pick in 2026?
Choose LMDeploy for teams optimizing quantized serving. Choose OpenLLM for going from model name to production endpoint fast.
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