# LMDeploy vs SGLang

**Leader by vioscaleAI score:** LMDeploy

| Attribute | LMDeploy | SGLang |
|---|---|---|
| **vioscaleAI score** | 55.9 (64% (medium)) | 50.4 (10% (low)) |
| activity.commits_last_30d | 66 | - |
| adoption.dependent_repos | 2 | - |
| adoption.github_stars | 8,104 | - |
| content.faq | `[{"answer":"A software framework that enables developers to compress, deploy, and serve large language models with quantization optimization, multiple inference engines, and compatibility across various model architectures. It is indexed under Model Serving.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"What is LMDeploy?","confidence":0.6},{"answer":"LMDeploy is open source, so it can be self-hosted and used at no licence cost. It is released under the Apache-2.0 licence. Pricing changes often, so verify at source before relying on it.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"Is LMDeploy free to use?","confidence":0.6},{"answer":"LMDeploy supports Windows and a command-line interface. Platforms we have not confirmed are simply not listed here rather than ruled out.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"What platforms does LMDeploy support?","confidence":0.6},{"answer":"Yes. LMDeploy can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://lmdeploy.readthedocs.io/en/latest/","question":"Can LMDeploy be self-hosted?","confidence":0.6},{"answer":"Yes. LMDeploy is published under the Apache-2.0 licence, a permissive licence that generally allows commercial use and modification. Licence terms can change between releases, so verify against the repository for the version you intend to use.","source":"https://github.com/internlm/lmdeploy","question":"Is LMDeploy open source?","confidence":0.95}]` | `[{"answer":"An open-source inference system designed for production deployments of large language and multimodal models, supporting both single-GPU and distributed cluster setups with emphasis on low latency and high throughput. It is indexed under Model Serving.","source":"https://sgl-project.github.io/","question":"What is SGLang?","confidence":0.6},{"answer":"SGLang is open source, so it can be self-hosted and used at no licence cost. Pricing changes often, so verify at source before relying on it.","source":"https://sgl-project.github.io/","question":"Is SGLang free to use?","confidence":0.6},{"answer":"Yes. SGLang can be deployed self-hosted, so it does not have to run on the vendor's infrastructure.","source":"https://sgl-project.github.io/","question":"Can SGLang be self-hosted?","confidence":0.6}]` |
| deployment.options | `{"self_hosted":true}` | `{"self_hosted":true}` |
| description.long | LMDeploy is a comprehensive toolkit enabling efficient compression, deployment, and serving of large language models across multiple machines and GPUs with advanced optimization techniques. | An open-source inference system designed for production deployments of large language and multimodal models, supporting both single-GPU and distributed cluster setups with emphasis on low latency and high throughput. |
| features.capabilities | `{"gpu_acceleration":true,"continuous_batching":true,"multi_model_serving":true,"multi_gpu_multi_node":true,"quantization_support":true}` | `{"open_source":true,"gpu_acceleration":true,"multi_gpu_multi_node":true,"openai_compatible_api":true,"multi_framework_support":true}` |
| integrations.count | 2 | 3 |
| integrations.list | `[{"name":"llm-compressor"},{"name":"OpenCompass"}]` | `[{"name":"Hugging Face"},{"name":"OpenAI"},{"name":"Slack","native":false}]` |
| language.primary | Python | - |
| license.spdx | Apache-2.0 | - |
| platform.support | `{"cli":true,"windows":true}` | - |
| pricing | `{"type":"open_source","freeTier":true,"sourceUrl":"https://lmdeploy.readthedocs.io/en/latest/","retrievedAt":"2026-08-20T12:17:08.568Z"}` | `{"type":"open_source","sourceUrl":"https://sgl-project.github.io/","retrievedAt":"2026-09-06T13:46:39.277Z"}` |
| pricing.free_tier | yes | - |
| pricing.model | open_source | open_source |
| pricing.price_level | free | free |
| release.cadence_days | 21 | - |
| release.history | `[{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.18.0","date":"2026-09-28T07:04:58Z","type":"stable","version":"v0.18.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.17.0","date":"2026-09-01T07:13:39Z","type":"stable","version":"v0.17.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.16.0","date":"2026-08-19T04:43:53Z","type":"stable","version":"v0.16.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.15.0","date":"2026-07-31T13:00:46Z","type":"stable","version":"v0.15.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.14.0","date":"2026-06-24T04:36:56Z","type":"stable","version":"v0.14.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/0.14.0a2","date":"2026-06-16T04:16:04Z","type":"prerelease","version":"0.14.0a2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/0.14.0a1","date":"2026-06-01T08:46:02Z","type":"prerelease","version":"0.14.0a1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.13.0","date":"2026-05-12T03:46:57Z","type":"stable","version":"v0.13.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.3","date":"2026-04-08T03:37:26Z","type":"stable","version":"v0.12.3"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.2","date":"2026-03-18T03:13:55Z","type":"stable","version":"v0.12.2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.1","date":"2026-02-13T09:02:12Z","type":"stable","version":"v0.12.1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.12.0","date":"2026-02-04T06:28:25Z","type":"stable","version":"v0.12.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.11.1","date":"2025-12-24T13:27:06Z","type":"stable","version":"v0.11.1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.11.0","date":"2025-12-04T06:20:38Z","type":"stable","version":"v0.11.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.2","date":"2025-10-28T11:32:42Z","type":"stable","version":"v0.10.2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.1","date":"2025-09-26T02:45:30Z","type":"stable","version":"v0.10.1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.0","date":"2025-09-09T05:05:09Z","type":"stable","version":"v0.10.0"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.9.2.post1","date":"2025-08-19T09:44:41Z","type":"stable","version":"v0.9.2.post1"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.9.2","date":"2025-07-26T10:00:25Z","type":"stable","version":"v0.9.2"},{"url":"https://github.com/InternLM/lmdeploy/releases/tag/v0.9.1","date":"2025-07-04T10:05:31Z","type":"stable","version":"v0.9.1"}]` | - |
| security.vulnerabilities | `{"count":10,"source":"https://advisories.ecosyste.ms/api/v1/advisories?ecosystem=pypi&package_name=lmdeploy&per_page=100","last_12m":8,"max_severity":"CRITICAL"}` | - |

## Capabilities (Model Serving)

| Capability | LMDeploy | SGLang |
|---|:--:|:--:|
| **Capabilities** |  |  |
| Continuous batching | ✓ | - |
| Dynamic batching | - | - |
| Multi framework support | - | ✓ |
| GPU acceleration | ✓ | ✓ |
| Multi GPU multi node | ✓ | ✓ |
| Quantization support | ✓ | - |
| Openai compatible API | - | ✓ |
| Autoscaling scale to zero | - | - |
| Multi model serving | ✓ | - |
| Canary ab rollout | - | - |
| Kubernetes native | - | - |
| Open source | - | ✓ |

*Source: vioscaleAI. Generated 2026-10-16T20:38:43.493Z. "-" = undocumented, not absent.*