Comparison
Forward vs ncnn
Verdict
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; pick ncnn if ncnn is a high-performance framework for deep learning inference on mobile platforms written in C++, supporting conversion from multiple DL frameworks via pnnx.
Markdown twin · Forward alternatives · ncnn alternatives
GraphCanon updated Oct 3, 2026
15views this month
Trust & integrity
| Signal | Forward | ncnn |
|---|---|---|
| Maintenance | Dormant (1707d since push) As of Oct 3, 2026 · github_public_v1 | Very active (1d since push) As of Oct 3, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Oct 3, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 3, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 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
- Forward
- A library for high performance deep learning inference on NVIDIA GPUs
- ncnn
- High-performance neural network inference framework optimized for mobile platforms
Stars
- Forward
- 557
- ncnn
- 24k
Forks
- Forward
- 63
- ncnn
- 4.5k
Open issues
- Forward
- 0
- ncnn
- 1.2k
Language
- Forward
- C++
- ncnn
- C++
Adopt for
- Forward
- Forward is an NVIDIA GPU-based high-performance deep learning inference library that converts popular framework models directly into TensorRT for optimized inference.
- ncnn
- ncnn is a high-performance framework for deep learning inference on mobile platforms written in C++, supporting conversion from multiple DL frameworks via pnnx.
Persona
- Forward
- -
- ncnn
- -
Runtime
- Forward
- -
- ncnn
- -
License
- Forward
- Other license type - specific terms not detailed here; consult repository for details on licensing implications and permissiveness of use and distribution.
- ncnn
- Other, details not specified within the provided repository content.
Last pushed
- Forward
- Jan 29, 2022
- ncnn
- Oct 1, 2026
Categories
- Forward
- Inference & Serving
- ncnn
- Inference & Serving
Trust and health
Maintenance
- Forward
- Dormant (18%)
- ncnn
- Very active (96%)
Days since push
- Forward
- 1707d
- ncnn
- 1d
Open issues (now)
- Forward
- 0
- ncnn
- 1.2k
Stars delta
- Forward
- +1 (30d)
- ncnn
- +261 (30d)
Open issues delta
- Forward
- 0 (30d)
- ncnn
- +8 (30d)
Full report
- Forward
- Trust report
- ncnn
- Trust report
Shared compatibility
- Python · Forward: Python runtime · ncnn: Python runtime
Choose Forward if…
- Tags unique to Forward: cuda, forward, gpu, inference-engine.
- When you need to quickly integrate TensorFlow, PyTorch, Keras, or ONNX models on NVIDIA GPUs for inference and require minimal conversion effort.
- Leaner open-issue backlog (0).
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.
Choose ncnn if…
- Requirements: Requires pnnx for exporting PyTorch models to ncnn..
- Tags unique to ncnn: android, arm-neon, artificial-intelligence, caffe.
- For users requiring fast inference speeds optimized for mobile devices such as Android and iOS.
When NOT to use ncnn
- If working in an environment where GPU acceleration on desktop or server is more beneficial than CPU efficiency.
- For tasks that demand extensive training within the framework itself, as ncnn focuses on inference rather than training capabilities.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Tencent/Forward) · observed Oct 3, 2026
- GitHub forks (Tencent/Forward) · observed Oct 3, 2026
- Last push (Tencent/Forward) · observed Jan 29, 2022
- License file (Other) · observed Oct 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Tencent/ncnn) · observed Oct 3, 2026
- GitHub forks (Tencent/ncnn) · observed Oct 3, 2026
- Last push (Tencent/ncnn) · observed Oct 1, 2026
- License file (Other) · observed Oct 3, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Forward 557 · ncnn 24k (synced Oct 3, 2026).
Common questions
- What is the difference between Forward and ncnn?
- Forward: A library for high performance deep learning inference on NVIDIA GPUs. ncnn: High-performance neural network inference framework optimized for mobile platforms. See the comparison table for live GitHub stats and shared categories.
- When should I choose Forward over ncnn?
- Choose Forward over ncnn when Tags unique to Forward: cuda, forward, gpu, inference-engine; When you need to quickly integrate TensorFlow, PyTorch, Keras, or ONNX models on NVIDIA GPUs for inference and require minimal conversion effort; Leaner open-issue backlog (0).
- When should I choose ncnn over Forward?
- Choose ncnn over Forward when Requirements: Requires pnnx for exporting PyTorch models to ncnn.; Tags unique to ncnn: android, arm-neon, artificial-intelligence, caffe; For users requiring fast inference speeds optimized for mobile devices such as Android and iOS.
- 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.
- When should I avoid ncnn?
- If working in an environment where GPU acceleration on desktop or server is more beneficial than CPU efficiency. For tasks that demand extensive training within the framework itself, as ncnn focuses on inference rather than training capabilities.
- Is Forward or ncnn more popular on GitHub?
- ncnn has more GitHub stars (23,905 vs 557). Stars measure visibility, not whether either tool fits your constraints.
- Are Forward and ncnn open source?
- Yes - both are open-source projects on GitHub (Forward: Other, ncnn: Other).
- Where can I find alternatives to Forward or ncnn?
- GraphCanon lists graph-backed alternatives at Forward alternatives and ncnn alternatives (Forward markdown twin, ncnn 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, Forward or ncnn?
- Forward: Dormant. ncnn: 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 Forward and ncnn?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Forward trust report; ncnn trust report.