--- title: "lmdeploy vs vllm-ascend" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/internlm-lmdeploy-vs-vllm-project-vllm-ascend" tools: ["internlm-lmdeploy", "vllm-project-vllm-ascend"] --- # lmdeploy vs vllm-ascend *GraphCanon updated Jul 12, 2026* ## Verdict Pick lmdeploy when lmdeploy is primarily Python; vllm-ascend is C++; pick vllm-ascend when vllm-ascend is primarily C++; lmdeploy is Python. [lmdeploy](https://lmdeploy.readthedocs.io/en/latest) reports 8.0k GitHub stars, 703 forks, and 597 open issues, last pushed Jul 10, 2026. [vllm-ascend](https://docs.vllm.ai/projects/ascend) has 2.5k stars, 1.7k forks, and 2.4k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [lmdeploy's repository](https://github.com/InternLM/lmdeploy) and [vllm-ascend's repository](https://github.com/vllm-project/vllm-ascend). | | [lmdeploy](/tools/internlm-lmdeploy.md) | [vllm-ascend](/tools/vllm-project-vllm-ascend.md) | | --- | --- | --- | | Tagline | LMDeploy is a toolkit for compressing, deploying, and serving LLMs. | Community maintained hardware plugin for vLLM on Ascend | | Stars | 7,952 | 2,477 | | Forks | 703 | 1,729 | | Open issues | 597 | 2,392 | | Language | Python | C++ | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache License 2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmdeploy](/tools/internlm-lmdeploy.md) | [vllm-ascend](/tools/vllm-project-vllm-ascend.md) | | --- | --- | --- | | Open issues (now) | 597 | 2.4k | | Security scan | No lockfile | 9 low (9 low) | | Full report | [trust report](/tools/internlm-lmdeploy/trust.md) | [trust report](/tools/vllm-project-vllm-ascend/trust.md) | ## Decision facts: vllm-ascend - **Hosting:** self hosted - **Pricing:** freemium - **License detail:** Apache License 2.0 ## Choose when ### Choose lmdeploy if… - lmdeploy is primarily Python; vllm-ascend is C++. - Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer. - More GitHub stars (8.0k vs 2.5k) - visibility, not fit. ### Choose vllm-ascend if… - vllm-ascend is primarily C++; lmdeploy is Python. - Tags unique to vllm-ascend: ascend, inference, llm, llm-serving. - vllm-ascend ships Docker support for self-hosted deployment. - - When you need to deploy large language models on Ascend hardware and leverage vLLM's ecosystem for inference and serving operations. ## When NOT to use lmdeploy - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use vllm-ascend - - When your infrastructure does not include or support Ascend chips, as vllm-ascend is specifically designed to work with Ascend hardware. - - For environments where flexibility in choosing the underlying hardware is crucial, because vllm-ascend limits this choice by its dependency on Ascend. ## Common questions ### What is the difference between lmdeploy and vllm-ascend? lmdeploy: LMDeploy is a toolkit for compressing, deploying, and serving LLMs.. vllm-ascend: Community maintained hardware plugin for vLLM on Ascend. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmdeploy over vllm-ascend? Choose lmdeploy over vllm-ascend when lmdeploy is primarily Python; vllm-ascend is C++; Tags unique to lmdeploy: codellama, cuda-kernels, deepspeed, fastertransformer; More GitHub stars (8.0k vs 2.5k) - visibility, not fit. ### When should I choose vllm-ascend over lmdeploy? Choose vllm-ascend over lmdeploy when vllm-ascend is primarily C++; lmdeploy is Python; Tags unique to vllm-ascend: ascend, inference, llm, llm-serving; vllm-ascend ships Docker support for self-hosted deployment; - When you need to deploy large language models on Ascend hardware and leverage vLLM's ecosystem for inference and serving operations. ### When should I avoid lmdeploy? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid vllm-ascend? - When your infrastructure does not include or support Ascend chips, as vllm-ascend is specifically designed to work with Ascend hardware. - For environments where flexibility in choosing the underlying hardware is crucial, because vllm-ascend limits this choice by its dependency on Ascend. ### Is lmdeploy or vllm-ascend more popular on GitHub? lmdeploy has more GitHub stars (7,952 vs 2,477). Stars measure visibility, not whether either tool fits your constraints. ### Are lmdeploy and vllm-ascend open source? Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, vllm-ascend: Apache-2.0). ### Where can I find alternatives to lmdeploy or vllm-ascend? GraphCanon lists graph-backed alternatives at [lmdeploy alternatives](/tools/internlm-lmdeploy/alternatives) and [vllm-ascend alternatives](/tools/vllm-project-vllm-ascend/alternatives) ([lmdeploy markdown twin](/tools/internlm-lmdeploy/alternatives.md), [vllm-ascend markdown twin](/tools/vllm-project-vllm-ascend/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-vllm-project-vllm-ascend.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmdeploy or vllm-ascend? lmdeploy: Very active. vllm-ascend: Very active. 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 vllm-ascend? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lmdeploy trust report](/tools/internlm-lmdeploy/trust); [vllm-ascend trust report](/tools/vllm-project-vllm-ascend/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/_