--- title: "lmdeploy vs deploy-llms-with-ansible" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lmdeploy-vs-xamey-deploy-llms-with-ansible" tools: ["internlm-lmdeploy", "xamey-deploy-llms-with-ansible"] --- # lmdeploy vs deploy-llms-with-ansible *GraphCanon updated Aug 7, 2026* ## Verdict Pick lmdeploy if lMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series; pick deploy-llms-with-ansible if deploy-llms-with-ansible. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) reports 8.0k GitHub stars, 723 forks, and 607 open issues, last pushed Aug 6, 2026. [deploy-llms-with-ansible](https://github.com/xamey/deploy-llms-with-ansible) has 3 stars, 0 forks, and 0 open issues, last pushed May 1, 2025. Figures are from public GitHub metadata via [lmdeploy's repository](https://github.com/InternLM/lmdeploy) and [deploy-llms-with-ansible's repository](https://github.com/xamey/deploy-llms-with-ansible). | | [lmdeploy](/tools/internlm-lmdeploy.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Tagline | Toolkit for compressing, deploying, and serving LLMs | Easily deploy LLMs using Ansible | | Stars | 7,995 | 3 | | Forks | 723 | 0 | | Open issues | 607 | 0 | | Language | Python | - | | Adopt for | LMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. | deploy-llms-with-ansible | | Persona | - | - | | Runtime | - | - | | License | Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. | - | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmdeploy](/tools/internlm-lmdeploy.md) | [deploy-llms-with-ansible](/tools/xamey-deploy-llms-with-ansible.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 462d | | Open issues (now) | 607 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/internlm-lmdeploy/trust.md) | [trust report](/tools/xamey-deploy-llms-with-ansible/trust.md) | ## Decision facts: lmdeploy - **Requirements:** Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13. - **Adopt for:** LMDeploy is focused on compressing and efficiently serving LLMs, making it suitable for teams already invested in CUDA environments like Nvidia's GeForce RTX 50 series. - **License detail:** Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms. ## Decision facts: deploy-llms-with-ansible - **Pricing:** unknown - **Requirements:** Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present. - **Adopt for:** deploy-llms-with-ansible ## Choose when ### Choose lmdeploy if… - Requirements: Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13.. - Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer. - When your team operates within a CUDA environment, such as using an Nvidia GeForce RTX 50 series GPU, because the default prebuilt wheels are optimized for CUDA 12.8. ### Choose deploy-llms-with-ansible if… - Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.. - Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp. - When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ## When NOT to use lmdeploy - When your infrastructure relies on software environments or GPUs not aligned with CUDA 12.8, as LMDeploy's default prebuilt wheels might require adjustments to operate optimally. - If you are working exclusively in non-Nvidia GPU ecosystems where LMDeploy's CUDA focus does not align with the hardware optimizations available. ## When NOT to use deploy-llms-with-ansible - When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. - If the infrastructure does not support or permit the use of Docker for containerizing applications. - In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ## Common questions ### What is the difference between lmdeploy and deploy-llms-with-ansible? lmdeploy: Toolkit for compressing, deploying, and serving LLMs. deploy-llms-with-ansible: Easily deploy LLMs using Ansible. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmdeploy over deploy-llms-with-ansible? Choose lmdeploy over deploy-llms-with-ansible when Requirements: Installation is optimized through pip in a Conda environment using Python versions between 3.10 and 3.13.; Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer; When your team operates within a CUDA environment, such as using an Nvidia GeForce RTX 50 series GPU, because the default prebuilt wheels are optimized for CUDA 12.8. ### When should I choose deploy-llms-with-ansible over lmdeploy? Choose deploy-llms-with-ansible over lmdeploy when Requirements: Requires Docker; Requires Ansible installed and configured on the local machine.; Debian-based VM with SSH access and Docker must be present.; Tags unique to deploy-llms-with-ansible: ansible, deployment, docker, llama-cpp; When you prefer using Ansible to automate the deployment of LLMs on a Debian-based virtual machine equipped with Docker. ### When should I avoid lmdeploy? When your infrastructure relies on software environments or GPUs not aligned with CUDA 12.8, as LMDeploy's default prebuilt wheels might require adjustments to operate optimally. If you are working exclusively in non-Nvidia GPU ecosystems where LMDeploy's CUDA focus does not align with the hardware optimizations available. ### When should I avoid deploy-llms-with-ansible? When working in an environment that uses alternative automation tools like Terraform or Chef, as this tool specifically requires Ansible knowledge. If the infrastructure does not support or permit the use of Docker for containerizing applications. In cases where extensive customization of models beyond what llama.cpp and Ollama offer is required. ### Is lmdeploy or deploy-llms-with-ansible more popular on GitHub? lmdeploy has more GitHub stars (7,995 vs 3). Stars measure visibility, not whether either tool fits your constraints. ### Are lmdeploy and deploy-llms-with-ansible open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to lmdeploy or deploy-llms-with-ansible? GraphCanon lists graph-backed alternatives at [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) and [deploy-llms-with-ansible alternatives](/tools/xamey-deploy-llms-with-ansible/alternatives) ([lmdeploy markdown twin](/tools/internlm-lmdeploy/alternatives.md), [deploy-llms-with-ansible markdown twin](/tools/xamey-deploy-llms-with-ansible/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/internlm-lmdeploy-vs-xamey-deploy-llms-with-ansible.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmdeploy or deploy-llms-with-ansible? lmdeploy: Very active. deploy-llms-with-ansible: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for lmdeploy and deploy-llms-with-ansible? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmdeploy trust report](/tools/internlm-lmdeploy/trust); [deploy-llms-with-ansible trust report](/tools/xamey-deploy-llms-with-ansible/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=internlm-lmdeploy`](/api/graphcanon/graph?tool=internlm-lmdeploy) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_