Comparison
nndeploy vs Forward
Verdict
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; pick Forward if forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference.
Markdown twin · nndeploy alternatives · Forward alternatives
GraphCanon updated Sep 20, 2026
11views this month
Trust & integrity
| Signal | nndeploy | Forward |
|---|---|---|
| Maintenance | Steady (32d since push) As of Sep 17, 2026 · github_public_v1 | Dormant (1678d since push) As of Sep 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 17, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 4, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 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
- nndeploy
- An Easy-to-Use and High-Performance AI Deployment Framework
- Forward
- A library for high performance deep learning inference on NVIDIA GPUs
Stars
- nndeploy
- 1.9k
- Forward
- 556
Forks
- nndeploy
- 233
- Forward
- 63
Open issues
- nndeploy
- 23
- Forward
- 0
Language
- nndeploy
- C++
- Forward
- C++
Adopt for
- 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.
- Forward
- Forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference.
Persona
- nndeploy
- -
- Forward
- -
Runtime
- nndeploy
- -
- Forward
- -
License
- nndeploy
- Apache-2.0
- Forward
- Other license type - specific terms not detailed here; consult repository for details on licensing implications and permissiveness of use and distribution.
Last pushed
- nndeploy
- Aug 15, 2026
- Forward
- Jan 29, 2022
Categories
- nndeploy
- Developer Tools, Inference & Serving
- Forward
- Inference & Serving
Trust and health
Maintenance
- nndeploy
- Steady (60%)
- Forward
- Dormant (18%)
Days since push
- nndeploy
- 32d
- Forward
- 1678d
Open issues (now)
- nndeploy
- 23
- Forward
- 0
Stars delta
- nndeploy
- +18 (30d)
- Forward
- 0 (30d)
OSV dependency advisories
- nndeploy
- Published findings
- Forward
- No lockfile (source not queried)
Full report
- nndeploy
- Trust report
- Forward
- Trust report
Shared compatibility
- Python · nndeploy: Python runtime · Forward: Python runtime
Choose nndeploy if…
- License: nndeploy is Apache-2.0, Forward is Other.
- Tags unique to nndeploy: ai, ascend, deployment, diffusers.
- 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.
Choose Forward if…
- License: Forward is Other, nndeploy is Apache-2.0.
- Tags unique to Forward: cuda, forward, gpu, inference.
- When you need to quickly integrate TensorFlow, PyTorch, Keras, or ONNX models on NVIDIA GPUs for inference and require minimal conversion effort.
When NOT to use Forward
- If your project requires model serving or inference on CPU-only environments, as Forward is optimized for NVIDIA GPUs.
- For users who need extensive customization beyond the supported models (TensorFlow, PyTorch, Keras, ONNX) as expanding support necessitates additional engineering effort.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (Tencent/Forward) · observed Sep 20, 2026
- GitHub forks (Tencent/Forward) · observed Sep 20, 2026
- Last push (Tencent/Forward) · observed Jan 29, 2022
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: nndeploy 1.9k · Forward 556 (synced Sep 20, 2026).
Common questions
- What is the difference between nndeploy and Forward?
- nndeploy: An Easy-to-Use and High-Performance AI Deployment Framework. Forward: A library for high performance deep learning inference on NVIDIA GPUs. See the comparison table for live GitHub stats and shared categories.
- When should I choose nndeploy over Forward?
- Choose nndeploy over Forward when License: nndeploy is Apache-2.0, Forward is Other; Tags unique to nndeploy: ai, ascend, deployment, diffusers; 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 choose Forward over nndeploy?
- Choose Forward over nndeploy when License: Forward is Other, nndeploy is Apache-2.0; Tags unique to Forward: cuda, forward, gpu, inference; When you need to quickly integrate TensorFlow, PyTorch, Keras, or ONNX models on NVIDIA GPUs for inference and require minimal conversion effort.
- 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.
- When should I avoid Forward?
- If your project requires model serving or inference on CPU-only environments, as Forward is optimized for NVIDIA GPUs. For users who need extensive customization beyond the supported models (TensorFlow, PyTorch, Keras, ONNX) as expanding support necessitates additional engineering effort.
- Is nndeploy or Forward more popular on GitHub?
- nndeploy has more GitHub stars (1,878 vs 556). Stars measure visibility, not whether either tool fits your constraints.
- Are nndeploy and Forward open source?
- Yes - both are open-source projects on GitHub (nndeploy: Apache-2.0, Forward: Other).
- Where can I find alternatives to nndeploy or Forward?
- GraphCanon lists graph-backed alternatives at nndeploy alternatives and Forward alternatives (nndeploy markdown twin, Forward 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, nndeploy or Forward?
- nndeploy: Steady. Forward: 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 nndeploy and Forward?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: nndeploy trust report; Forward trust report.