SGLang vs 
LMDeploy
SGLang vs LMDeploy compared for 2026 — features, license, ease of use, performance and which one to choose. Fast serving with structured outputs vs Toolkit for compressing and serving LLMs.
Updated regularly · curated by olud.ai
SGLang vs LMDeploy at a glance
| Spec | SGLang | 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 | teams needing structured-output serving | teams optimizing quantized serving |
| GitHub stars | 30.6k | 8k |
Feature comparison
| Feature | SGLang | LMDeploy |
|---|---|---|
| OpenAI-compatible API | ✓ | ✓ |
| Continuous batching | ✓ | ✓ |
| Quantization | ✓ | ✓ |
| Multi-GPU | ✓ | ✓ |
| Structured output | ✓ | ✗ |
| Docker | ✓ | ✓ |
How SGLang and LMDeploy score
| Criterion | SGLang | LMDeploy |
|---|---|---|
| Popularity | 4.0 | 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
SGLang
SGLang is a fast serving framework for LLMs and vision-language models, featuring RadixAttention and strong support for structured and programmatic generation.
- Very fast with RadixAttention caching
- First-class structured / programmatic generation
- Strong vision-language model support
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
SGLang is inference server, while LMDeploy is inference server. In short, SGLang fits teams needing structured-output serving, and LMDeploy fits teams optimizing quantized serving.
Which should you choose?
Choose SGLang for teams needing structured-output serving. 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 SGLang or LMDeploy easier to use?
Both sit at a similar level (Advanced). Your choice should come down to fit rather than difficulty.
Are SGLang and LMDeploy free?
SGLang 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 SGLang and LMDeploy locally?
SGLang: self-hosted · LMDeploy: self-hosted. Both can be used without sending your data to a third-party cloud where their setup allows.
SGLang vs LMDeploy — which should I pick in 2026?
Choose SGLang for teams needing structured-output serving. Choose LMDeploy for teams optimizing quantized serving.
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