Comparison
yalm vs airllm
Verdict
Pick yalm if yALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries; 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.
Markdown twin · yalm alternatives · airllm alternatives
GraphCanon updated 3w
Trust & integrity
| Signal | yalm | airllm |
|---|---|---|
| Maintenance | Slowing (315d since push) As of 1mo · github_public_v1 | Very active (5d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1mo · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- yalm
- LLM inference engine in C++/CUDA without dependency on external libraries except for I/O
- airllm
- AirLLM 70B inference with single 4GB GPU
Stars
- yalm
- 592
- airllm
- 24k
Forks
- yalm
- 64
- airllm
- 2.7k
Open issues
- yalm
- 4
- airllm
- 115
Language
- yalm
- C++
- airllm
- Jupyter Notebook
Adopt for
- yalm
- YALM offers a no-frills LLM inference engine in C++/CUDA, optimized for tasks requiring minimal external dependencies beyond I/O and no reliance on heavyweight ML libraries.
- airllm
- AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU.
Persona
- yalm
- -
- airllm
- -
Runtime
- yalm
- -
- airllm
- -
License
- yalm
- -
- airllm
- Apache-2.0
Last pushed
- yalm
- Sep 13, 2025
- airllm
- Jul 23, 2026
Categories
- yalm
- Inference & Serving
- airllm
- Inference & Serving
Trust and health
Maintenance
- yalm
- Slowing (36%)
- airllm
- Very active (96%)
Days since push
- yalm
- 315d
- airllm
- 5d
Open issues (now)
- yalm
- 4
- airllm
- 115
OSV dependency advisories
- yalm
- No lockfile (source not queried)
- airllm
- Published findings
Full report
- yalm
- Trust report
- airllm
- Trust report
Choose yalm if…
- yalm is primarily C++; airllm is Jupyter Notebook.
- Tags unique to yalm: cpp, cuda, llm-inference, machine-learning.
- When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies
When NOT to use yalm
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs
- For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
Choose airllm if…
- airllm is primarily Jupyter Notebook; yalm is C++.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (andrewkchan/yalm) · observed Jul 25, 2026
- GitHub forks (andrewkchan/yalm) · observed Jul 25, 2026
- Last push (andrewkchan/yalm) · observed Sep 13, 2025
- License file (unknown) · observed Jul 25, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: yalm 592 · airllm 24k (synced Jul 25, 2026).
Common questions
- What is the difference between yalm and airllm?
- yalm: LLM inference engine in C++/CUDA without dependency on external libraries except for I/O. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories.
- When should I choose yalm over airllm?
- Choose yalm over airllm when yalm is primarily C++; airllm is Jupyter Notebook; Tags unique to yalm: cpp, cuda, llm-inference, machine-learning; When your project's stack is primarily based on C++ and CUDA, allowing seamless integration without additional dependencies.
- When should I choose airllm over yalm?
- Choose airllm over yalm when airllm is primarily Jupyter Notebook; yalm is C++; 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 avoid yalm?
- If extensive functionality or ease of use from other ML libraries is required, as YALM does not support dependencies beyond I/O needs For developers who prefer tools with broader community support and more comprehensive feature sets, given that YALM specializes in a narrow scope
- 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.
- Is yalm or airllm more popular on GitHub?
- airllm has more GitHub stars (24,183 vs 592). Stars measure visibility, not whether either tool fits your constraints.
- Are yalm and airllm open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to yalm or airllm?
- GraphCanon lists graph-backed alternatives at yalm alternatives and airllm alternatives (yalm markdown twin, airllm 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, yalm or airllm?
- yalm: Slowing. airllm: 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 yalm and airllm?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: yalm trust report; airllm trust report.