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LMDeploy and vLLM compared side by side on GitHub stars, downloads, language, license, capabilities, strengths and trade-offs.
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Comparing inference engines is the process of evaluating two or three competing tools side by side on live community signals, technical capabilities, language, license, and trade-offs so a team can pick the one that fits its hardware and workload.
Start with the constraints that are not negotiable: the hardware you already run on, the license your legal team will sign off on, and the kind of workload you need to serve. An engine that cannot use your GPU or fit your model is not really an option, no matter how popular it is.
Then look at live signals: GitHub stars and contributors show whether the project is gathering momentum, PyPI downloads show whether teams are actually shipping with it, and last-commit date shows whether the maintainers are still around. Capability flags like OpenAI-compatible API, GPU support, quantization, continuous batching, and multi-GPU narrow the field to engines that match your job-to-be-done.
Finish by reading the strengths and trade-offs columns side by side. The simplest engine that covers your real requirements almost always beats the most powerful one. Copy the share link once you have a comparison you trust so you can revisit it during planning.
Decisions about inference engines rarely happen in isolation. Pair this comparator with the directories and benchmarks that ground the rest of the stack.
Live data from GitHub and the package registries, refreshed every day.
GitHub stars
Contributors
Forks
PyPI downloads per month
Pick LMDeploy if you need: High-throughput serving with built-in quantization
Full LMDeploy profileOpen and closed model rankings with benchmarks, context windows, modalities, and live API prices.
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vLLM is more popular on GitHub with 93.1K stars, against 8.1K for LMDeploy. Stars measure developer interest, so also compare downloads and contributors above.
LMDeploy has 157 contributors and its last commit was 5 days ago. vLLM has 3.6K contributors and its last commit was Today.
vLLM has AMD GPU built in. LMDeploy does not, based on each project’s documentation. See the capabilities table above for the full list.
LMDeploy is a Mixed engine from InternLM, released under the Apache 2.0 license. vLLM is a Python engine from vLLM Project, released under the Apache 2.0 license. The table above shows how they differ on hardware support, APIs and serving features.
Start with your hardware and your traffic. Check which engine supports your GPUs, whether you need an OpenAI-compatible API, and how many users it must serve at once. Then compare community activity, since an active project ships fixes faster.
