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Comparison

airllm vs prompt-patterns

Verdict

Pick airllm when requirements: Min 4 GB RAM; Installation is straightforward via pip.; pick prompt-patterns when requirements: The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information..

Markdown twin · airllm alternatives · prompt-patterns alternatives

GraphCanon updated Sep 20, 2026

7views this month

airllm logo

airllm

lyogavin/airllm

★ 35kpushed Sep 18, 2026
vs
prompt-patterns logo

prompt-patterns

phodal/prompt-patterns

★ 3.1kpushed Mar 22, 2023

Trust & integrity

Signalairllmprompt-patterns
Maintenance
Very active (0d since push)
As of Sep 18, 2026 · github_public_v1
Dormant (1278d since push)
As of Sep 20, 2026 · github_public_v1
Provenance
Not a fork · Personal account
As of Sep 18, 2026 · github_public_v1
Not a fork · Personal account
As of Sep 20, 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
airllm trust reportprompt-patterns trust reportTrust methodology

Tagline

airllm
Inference for AirLLM 70B model with single 4GB GPU
prompt-patterns
Prompt 编写模式：如何将思维框架赋予机器，以设计模式的形式来思考 prompt

Stars

airllm
35k
prompt-patterns
3.1k

Forks

airllm
3.6k
prompt-patterns
198

Open issues

airllm
155
prompt-patterns
0

Language

airllm
Jupyter Notebook
prompt-patterns
-

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.
prompt-patterns
-

Persona

airllm
-
prompt-patterns
-

Runtime

airllm
-
prompt-patterns
-

License

airllm
Apache-2.0
prompt-patterns
-

Last pushed

airllm
Sep 18, 2026
prompt-patterns
Mar 22, 2023

Categories

airllm
Inference & Serving, LLM Frameworks
prompt-patterns
Evaluation & Observability, LLM Frameworks

Trust and health

Maintenance

airllm
Very active (96%)
prompt-patterns
Dormant (18%)

Days since push

airllm
0d
prompt-patterns
1278d

Open issues (now)

airllm
155
prompt-patterns
0

Stars delta

airllm
+10k (30d)
prompt-patterns
-4 (30d)

Open issues delta

airllm
+40 (30d)
prompt-patterns
0 (30d)

OSV dependency advisories

airllm
Published findings
prompt-patterns
No lockfile (source not queried)

Full report

airllm
Trust report
prompt-patterns
Trust report

Choose airllm if…

  • Requirements: Min 4 GB RAM; Installation is straightforward via pip..
  • Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai.
  • Also covers Inference & Serving.
  • 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.
GitHubDetails

Choose prompt-patterns if…

  • Requirements: The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information..
  • Tags unique to prompt-patterns: chatgpt, github-copilot, prompt-engineering, stable-diffusion.
  • Also covers Evaluation & Observability.
  • Use prompt-patterns for designing structured prompts to guide AI thinking in specific frameworks when working on projects that require maintaining a clear cognitive structure.

When NOT to use prompt-patterns

  • Avoid prompt-patterns for real-time applications or situations requiring dynamic, contextually adaptive prompts, as it might lack flexibility compared to more generalized frameworks.
  • Do not use if your project's requirements revolve around innovative and unstructured AI interactions; this tool is best for structured environments.
GitHubDetails

Explore

airllm trust report →prompt-patterns trust report →Inference & Serving category →LLM Frameworks category →Evaluation & Observability category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: airllm 35k · prompt-patterns 3.1k (synced Sep 20, 2026).

Common questions

What is the difference between airllm and prompt-patterns?
airllm: Inference for AirLLM 70B model with single 4GB GPU. prompt-patterns: Prompt 编写模式：如何将思维框架赋予机器，以设计模式的形式来思考 prompt. See the comparison table for live GitHub stats and shared categories.
When should I choose airllm over prompt-patterns?
Choose airllm over prompt-patterns when Requirements: Min 4 GB RAM; Installation is straightforward via pip.; Tags unique to airllm: chinese-llm, chinese-nlp, finetune, generative-ai; Also covers Inference & Serving; 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 prompt-patterns over airllm?
Choose prompt-patterns over airllm when Requirements: The repository does not specify system requirements.; Details about compatible AI models, frameworks, and the expected environment setup remain unspecified in the provided information.; Tags unique to prompt-patterns: chatgpt, github-copilot, prompt-engineering, stable-diffusion; Also covers Evaluation & Observability; Use prompt-patterns for designing structured prompts to guide AI thinking in specific frameworks when working on projects that require maintaining a clear cognitive structure.
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 prompt-patterns?
Avoid prompt-patterns for real-time applications or situations requiring dynamic, contextually adaptive prompts, as it might lack flexibility compared to more generalized frameworks. Do not use if your project's requirements revolve around innovative and unstructured AI interactions; this tool is best for structured environments.
Is airllm or prompt-patterns more popular on GitHub?
airllm has more GitHub stars (34,504 vs 3,091). Stars measure visibility, not whether either tool fits your constraints.
Are airllm and prompt-patterns open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to airllm or prompt-patterns?
GraphCanon lists graph-backed alternatives at airllm alternatives and prompt-patterns alternatives (airllm markdown twin, prompt-patterns 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 prompt-patterns?
airllm: Very active. prompt-patterns: 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 prompt-patterns?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airllm trust report; prompt-patterns trust report.

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