Comparison
airllm vs TNN
Verdict
Pick airllm if airLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI; pick TNN if developed by Tencent Labs, TNN offers strong cross-platform performance with efficient model compression and runtime optimization for mobile to server use.
Markdown twin · airllm alternatives · TNN alternatives
GraphCanon updated Oct 3, 2026
9views this month
Trust & integrity
| Signal | airllm | TNN |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Dormant (511d since push) As of Oct 3, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 3, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Sep 18, 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
- airllm
- Inference for AirLLM 70B model with single 4GB GPU
- TNN
- A cross-platform deep learning inference framework for diverse computing environments, from mobile to desktop and server.
Stars
- airllm
- 35k
- TNN
- 4.7k
Forks
- airllm
- 3.6k
- TNN
- 771
Open issues
- airllm
- 155
- TNN
- 320
Language
- airllm
- Jupyter Notebook
- TNN
- C++
Adopt for
- airllm
- AirLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI.
- TNN
- Developed by Tencent Labs, TNN offers strong cross-platform performance with efficient model compression and runtime optimization for mobile to server use.
Persona
- airllm
- -
- TNN
- -
Runtime
- airllm
- -
- TNN
- -
License
- airllm
- Apache-2.0
- TNN
- Other
Last pushed
- airllm
- Sep 18, 2026
- TNN
- May 9, 2025
Categories
- airllm
- Inference & Serving, LLM Frameworks
- TNN
- Inference & Serving
Trust and health
Maintenance
- airllm
- Very active (96%)
- TNN
- Dormant (18%)
Days since push
- airllm
- 0d
- TNN
- 511d
Open issues (now)
- airllm
- 155
- TNN
- 320
Stars delta
- airllm
- +10k (30d)
- TNN
- +14 (30d)
Open issues delta
- airllm
- +40 (30d)
- TNN
- +2 (30d)
Owner type
- airllm
- User
- TNN
- Organization
OSV dependency advisories
- airllm
- Published findings
- TNN
- No lockfile (source not queried)
Full report
- airllm
- Trust report
- TNN
- Trust report
Choose airllm if…
- airllm is primarily Jupyter Notebook; TNN is C++.
- License: airllm is Apache-2.0, TNN is Other.
- Requirements: Min 4 GB RAM; Installation is straightforward via pip..
- Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
- Also covers LLM Frameworks.
- When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU.
When NOT to use airllm
- If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model.
- For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas.
Choose TNN if…
- TNN is primarily C++; airllm is Jupyter Notebook.
- License: TNN is Other, airllm is Apache-2.0.
- Tags unique to TNN: coreml, deep-learning, face-detection, hairsegmentaion.
- TNN ships Docker support for self-hosted deployment.
- When developing AI apps for Tencent-affiliated software like Mobile QQ or Weishi
When NOT to use TNN
- If you prefer a framework that heavily integrates with TensorFlow's ecosystem, as TNN has a steeper learning curve when converting models
- When your project primarily relies on Python environments. TNN is C++-centric with no native Python interface.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (lyogavin/airllm) · observed Sep 20, 2026
- GitHub forks (lyogavin/airllm) · observed Sep 20, 2026
- Last push (lyogavin/airllm) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (Tencent/TNN) · observed Oct 3, 2026
- GitHub forks (Tencent/TNN) · observed Oct 3, 2026
- Last push (Tencent/TNN) · observed May 9, 2025
- 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: airllm 35k · TNN 4.7k (synced Sep 20, 2026).
Common questions
- What is the difference between airllm and TNN?
- airllm: Inference for AirLLM 70B model with single 4GB GPU. TNN: A cross-platform deep learning inference framework for diverse computing environments, from mobile to desktop and server.. See the comparison table for live GitHub stats and shared categories.
- When should I choose airllm over TNN?
- Choose airllm over TNN when airllm is primarily Jupyter Notebook; TNN is C++; License: airllm is Apache-2.0, TNN is Other; Requirements: Min 4 GB RAM; Installation is straightforward via pip.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; Also covers LLM Frameworks; When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU.
- When should I choose TNN over airllm?
- Choose TNN over airllm when TNN is primarily C++; airllm is Jupyter Notebook; License: TNN is Other, airllm is Apache-2.0; Tags unique to TNN: coreml, deep-learning, face-detection, hairsegmentaion; TNN ships Docker support for self-hosted deployment; When developing AI apps for Tencent-affiliated software like Mobile QQ or Weishi.
- When should I avoid airllm?
- If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model. For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas.
- When should I avoid TNN?
- If you prefer a framework that heavily integrates with TensorFlow's ecosystem, as TNN has a steeper learning curve when converting models When your project primarily relies on Python environments. TNN is C++-centric with no native Python interface.
- Is airllm or TNN more popular on GitHub?
- airllm has more GitHub stars (34,504 vs 4,657). Stars measure visibility, not whether either tool fits your constraints.
- Are airllm and TNN open source?
- Yes - both are open-source projects on GitHub (airllm: Apache-2.0, TNN: Other).
- Where can I find alternatives to airllm or TNN?
- GraphCanon lists graph-backed alternatives at airllm alternatives and TNN alternatives (airllm markdown twin, TNN 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, airllm or TNN?
- airllm: Very active. TNN: 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 airllm and TNN?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airllm trust report; TNN trust report.