Home/Compare/lmdeploy vs vllm-ascend

Comparison

lmdeploy vs vllm-ascend

Verdict

Pick lmdeploy when lmdeploy is primarily Python; vllm-ascend is C++; pick vllm-ascend when vllm-ascend is primarily C++; lmdeploy is Python.

Markdown twin · lmdeploy alternatives · vllm-ascend alternatives

GraphCanon updated today

lmdeploy logo

lmdeploy

InternLM/lmdeploy

8.0kpushed Jul 10, 2026
vs
vllm-ascend logo

vllm-ascend

vllm-project/vllm-ascend

2.5kpushed Jul 11, 2026

Trust & integrity

Signallmdeployvllm-ascend
Maintenance
Very active (0d since push)
As of today · github_public_v1
Very active (0d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
9 low (9 low)
As of today · osv@v1

Tagline

lmdeploy
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
vllm-ascend
Community maintained hardware plugin for vLLM on Ascend

Stars

lmdeploy
8.0k
vllm-ascend
2.5k

Forks

lmdeploy
703
vllm-ascend
1.7k

Open issues

lmdeploy
597
vllm-ascend
2.4k

Language

lmdeploy
Python
vllm-ascend
C++

Adopt for

lmdeploy
-
vllm-ascend
-

Persona

lmdeploy
-
vllm-ascend
-

Runtime

lmdeploy
-
vllm-ascend
-

License

lmdeploy
Apache-2.0
vllm-ascend
Apache License 2.0

Last pushed

lmdeploy
Jul 10, 2026
vllm-ascend
Jul 11, 2026

Categories

lmdeploy
LLM Frameworks, Model Training, Inference & Serving
vllm-ascend
LLM Frameworks, Model Training, Inference & Serving

Trust and health

Open issues (now)

lmdeploy
597
vllm-ascend
2.4k

Security scan

lmdeploy
No lockfile
vllm-ascend
9 low (9 low)

Full report

lmdeploy
Trust report
vllm-ascend
Trust report

Choose lmdeploy if…

  • lmdeploy is primarily Python; vllm-ascend is C++.
  • Tags unique to lmdeploy: codellama, llama, deepspeed, fastertransformer.
  • More GitHub stars (8.0k vs 2.5k) - visibility, not fit.

When NOT to use lmdeploy

  • 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.
  • Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.

Choose vllm-ascend if…

  • vllm-ascend is primarily C++; lmdeploy is Python.
  • Tags unique to vllm-ascend: llmops, ascend, llm, model-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 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.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: lmdeploy 8.0k · vllm-ascend 2.5k (synced Jul 11, 2026).

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, llama, 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: llmops, ascend, llm, model-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?
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. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
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 and vllm-ascend alternatives (lmdeploy markdown twin, vllm-ascend markdown twin), 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 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; vllm-ascend trust report.