Comparison
airllm vs TNN
Verdict
Pick airllm if airLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU; 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 2w
Trust & integrity
| Signal | airllm | TNN |
|---|---|---|
| Maintenance | Very active (5d since push) As of 3w · github_public_v1 | Dormant (452d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- AirLLM 70B inference with single 4GB GPU
- TNN
- A cross-platform deep learning inference framework for diverse computing environments, from mobile to desktop and server.
Stars
- airllm
- 24k
- TNN
- 4.6k
Forks
- airllm
- 2.7k
- TNN
- 772
Open issues
- airllm
- 115
- TNN
- 318
Language
- airllm
- Jupyter Notebook
- TNN
- C++
Adopt for
- airllm
- AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
- 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
- Jul 23, 2026
- TNN
- May 9, 2025
Categories
- airllm
- Inference & Serving
- TNN
- Inference & Serving
Trust and health
Maintenance
- airllm
- Very active (96%)
- TNN
- Dormant (18%)
Days since push
- airllm
- 5d
- TNN
- 452d
Open issues (now)
- airllm
- 115
- TNN
- 318
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.
- Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply..
- Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences..
- Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
- If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
When NOT to use airllm
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency.
- Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
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 Jul 28, 2026
- GitHub forks (lyogavin/airllm) · observed Jul 28, 2026
- Last push (lyogavin/airllm) · observed Jul 23, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 9, 2026
- GitHub stars (Tencent/TNN) · observed Aug 4, 2026
- GitHub forks (Tencent/TNN) · observed Aug 4, 2026
- Last push (Tencent/TNN) · observed May 9, 2025
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: airllm 24k · TNN 4.6k (synced Jul 28, 2026).
Common questions
- What is the difference between airllm and TNN?
- airllm: AirLLM 70B inference 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; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM.
- 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?
- Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition.
- 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 (24,183 vs 4,643). 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.