LMDeploy vs 
Ray Serve
LMDeploy vs Ray Serve compared for 2026 — features, license, ease of use, performance and which one to choose. Toolkit for compressing and serving LLMs vs Scale model serving across a cluster.
Updated regularly · curated by olud.ai
LMDeploy vs Ray Serve at a glance
| Spec | LMDeploy | Ray Serve |
|---|---|---|
| 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 | Advanced |
| Best for | teams optimizing quantized serving | multi-model production pipelines at scale |
| GitHub stars | 8k | 43.3k |
How LMDeploy and Ray Serve score
| Criterion | LMDeploy | Ray Serve |
|---|---|---|
| Popularity | 2.5 | 4.0 |
| Maintenance | 5.0 | 5.0 |
| Ease of use | 2.5 | 2.5 |
| 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
Ray Serve
Ray Serve is a scalable model-serving library that composes multiple models and Python business logic into one deployment, scaling across a Ray cluster.
- Composes several models in one pipeline
- Autoscaling across a cluster
- Framework-agnostic
Key differences
LMDeploy is inference server, while Ray Serve is serving framework. They also differ in how they run (Self-hosted vs Yes). In short, LMDeploy fits teams optimizing quantized serving, and Ray Serve fits multi-model production pipelines at scale.
Which should you choose?
Choose LMDeploy for teams optimizing quantized serving. Choose Ray Serve for multi-model production pipelines at scale.
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 Ray Serve easier to use?
Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.
Are LMDeploy and Ray Serve free?
LMDeploy is free and open source (Apache-2.0), and Ray Serve is free and open source (Apache-2.0). Neither charges for the core software.
Can I run LMDeploy and Ray Serve locally?
LMDeploy: self-hosted · Ray Serve: yes. Both can be used without sending your data to a third-party cloud where their setup allows.
LMDeploy vs Ray Serve — which should I pick in 2026?
Choose LMDeploy for teams optimizing quantized serving. Choose Ray Serve for multi-model production pipelines at scale.
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