Comparison
lmdeploy vs nndeploy
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 nndeploy if nndeploy provides an easy-to-use and high-performance framework for deploying AI algorithms across various platforms with support for multiple deep learning frameworks.
Markdown twin · lmdeploy alternatives · nndeploy alternatives
GraphCanon updated Sep 20, 2026
12views this month
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Trust & integrity
| Signal | lmdeploy | nndeploy |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 7, 2026 · github_public_v1 | Steady (32d since push) As of Sep 17, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 17, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 15, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- lmdeploy
- Toolkit for compressing, deploying, and serving LLMs
- nndeploy
- An Easy-to-Use and High-Performance AI Deployment Framework
Stars
- lmdeploy
- 8.0k
- nndeploy
- 1.9k
Forks
- lmdeploy
- 737
- nndeploy
- 233
Open issues
- lmdeploy
- 608
- nndeploy
- 23
Language
- lmdeploy
- Python
- nndeploy
- C++
Adopt for
- lmdeploy
- 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.
- nndeploy
- nndeploy provides an easy-to-use and high-performance framework for deploying AI algorithms across various platforms with support for multiple deep learning frameworks.
Persona
- lmdeploy
- -
- nndeploy
- -
Runtime
- lmdeploy
- -
- nndeploy
- -
License
- lmdeploy
- Licensed under Apache-2.0, enabling flexible use and modification for both commercial and open-source projects, provided that users comply with its terms.
- nndeploy
- Apache-2.0
Last pushed
- lmdeploy
- Sep 5, 2026
- nndeploy
- Aug 15, 2026
Categories
- lmdeploy
- Inference & Serving
- nndeploy
- Developer Tools, Inference & Serving
Trust and health
Maintenance
- lmdeploy
- Very active (96%)
- nndeploy
- Steady (60%)
Days since push
- lmdeploy
- 1d
- nndeploy
- 32d
Open issues (now)
- lmdeploy
- 608
- nndeploy
- 23
Stars delta
- lmdeploy
- +49 (30d)
- nndeploy
- +18 (30d)
Open issues delta
- lmdeploy
- +1 (30d)
- nndeploy
- 0 (30d)
OSV dependency advisories
- lmdeploy
- No lockfile (source not queried)
- nndeploy
- Published findings
Full report
- lmdeploy
- Trust report
- nndeploy
- Trust report
Shared compatibility
- Python · lmdeploy: Python runtime · nndeploy: Python runtime
Choose lmdeploy if…
- lmdeploy is primarily Python; nndeploy is C++.
- 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 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.
Choose nndeploy if…
- nndeploy is primarily C++; lmdeploy is Python.
- Tags unique to nndeploy: ai, ascend, deep-learning, deployment.
- Also covers Developer Tools.
- If you need a tool that supports both Python and C++ custom nodes, integrating well into visual workflow design.
When NOT to use nndeploy
- Do not use if your project only requires low-level access to hardware without the need for a high-level interface like nndeploy offers.
- If you're working exclusively on platforms or frameworks unsupported by nndeploy, such as certain proprietary or non-mainstream inference engines.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (InternLM/lmdeploy) · observed Sep 20, 2026
- GitHub forks (InternLM/lmdeploy) · observed Sep 20, 2026
- Last push (InternLM/lmdeploy) · observed Sep 5, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (nndeploy/nndeploy) · observed Sep 20, 2026
- GitHub forks (nndeploy/nndeploy) · observed Sep 20, 2026
- Last push (nndeploy/nndeploy) · observed Aug 15, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: lmdeploy 8.0k · nndeploy 1.9k (synced Sep 20, 2026).
Common questions
- What is the difference between lmdeploy and nndeploy?
- lmdeploy: Toolkit for compressing, deploying, and serving LLMs. nndeploy: An Easy-to-Use and High-Performance AI Deployment Framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose lmdeploy over nndeploy?
- Choose lmdeploy over nndeploy when lmdeploy is primarily Python; nndeploy is C++; 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 nndeploy over lmdeploy?
- Choose nndeploy over lmdeploy when nndeploy is primarily C++; lmdeploy is Python; Tags unique to nndeploy: ai, ascend, deep-learning, deployment; Also covers Developer Tools; If you need a tool that supports both Python and C++ custom nodes, integrating well into visual workflow design.
- 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 nndeploy?
- Do not use if your project only requires low-level access to hardware without the need for a high-level interface like nndeploy offers. If you're working exclusively on platforms or frameworks unsupported by nndeploy, such as certain proprietary or non-mainstream inference engines.
- Is lmdeploy or nndeploy more popular on GitHub?
- lmdeploy has more GitHub stars (8,044 vs 1,878). Stars measure visibility, not whether either tool fits your constraints.
- Are lmdeploy and nndeploy open source?
- Yes - both are open-source projects on GitHub (lmdeploy: Apache-2.0, nndeploy: Apache-2.0).
- Where can I find alternatives to lmdeploy or nndeploy?
- GraphCanon lists graph-backed alternatives at lmdeploy alternatives and nndeploy alternatives (lmdeploy markdown twin, nndeploy 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 nndeploy?
- lmdeploy: Very active. nndeploy: Steady. 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 nndeploy?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmdeploy trust report; nndeploy trust report.