Comparison
airllm vs private-gpt
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 private-gpt if privateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,.
Markdown twin · airllm alternatives · private-gpt alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | airllm | private-gpt |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | 4 low (4 low) As of 2d · osv@v1 | No lockfile As of today · none |
Tagline
- airllm
- AirLLM 70B inference with single 4GB GPU
- private-gpt
- Complete API layer for private AI applications on local models
Stars
- airllm
- 22k
- private-gpt
- 57k
Forks
- airllm
- 2.6k
- private-gpt
- 7.6k
Open issues
- airllm
- 106
- private-gpt
- 5
Language
- airllm
- Jupyter Notebook
- private-gpt
- Python
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.
- private-gpt
- PrivateGPT provides a comprehensive API layer to build private, on-premise AI applications leveraging local OpenAI-compatible inference servers. It offers features such as RAG, skills, tools, text-to-SQL functionalities,
Persona
- airllm
- -
- private-gpt
- -
Runtime
- airllm
- -
- private-gpt
- -
License
- airllm
- Apache-2.0
- private-gpt
- Apache-2.0
Last pushed
- airllm
- Jul 11, 2026
- private-gpt
- Jul 10, 2026
Categories
- airllm
- Inference & Serving
- private-gpt
- Inference & Serving
Trust and health
Open issues (now)
- airllm
- 106
- private-gpt
- 5
Owner type
- airllm
- User
- private-gpt
- Organization
Security scan
- airllm
- 4 low (4 low)
- private-gpt
- No lockfile
Full report
- airllm
- Trust report
- private-gpt
- Trust report
Choose airllm if…
- airllm is primarily Jupyter Notebook; private-gpt is Python.
- 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: llama, chinese llm, llm, instruct-gpt.
- 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 private-gpt if…
- private-gpt is primarily Python; airllm is Jupyter Notebook.
- Requirements: Min 8 GB RAM; Requires Docker.
- Tags unique to private-gpt: text-to-sql, ai, on-premise, tools.
- private-gpt ships Docker support for self-hosted deployment.
- - You need to deploy and operationalize your own locally-run models without relying on cloud APIs.
When NOT to use private-gpt
- - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services.
- - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations.
- - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively.
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 11, 2026
- GitHub forks (lyogavin/airllm) · observed Jul 11, 2026
- Last push (lyogavin/airllm) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 9, 2026
- GitHub stars (zylon-ai/private-gpt) · observed Jul 11, 2026
- GitHub forks (zylon-ai/private-gpt) · observed Jul 11, 2026
- Last push (zylon-ai/private-gpt) · observed Jul 10, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: airllm 22k · private-gpt 57k (synced Jul 11, 2026).
Common questions
- What is the difference between airllm and private-gpt?
- airllm: AirLLM 70B inference with single 4GB GPU. private-gpt: Complete API layer for private AI applications on local models. See the comparison table for live GitHub stats and shared categories.
- When should I choose airllm over private-gpt?
- Choose airllm over private-gpt when airllm is primarily Jupyter Notebook; private-gpt is Python; 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: llama, chinese llm, llm, instruct-gpt; 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 private-gpt over airllm?
- Choose private-gpt over airllm when private-gpt is primarily Python; airllm is Jupyter Notebook; Requirements: Min 8 GB RAM; Requires Docker; Tags unique to private-gpt: text-to-sql, ai, on-premise, tools; private-gpt ships Docker support for self-hosted deployment; - You need to deploy and operationalize your own locally-run models without relying on cloud APIs.
- 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 private-gpt?
- - You prefer simplicity and ease-of-use over full control; PrivateGPT requires more setup than using direct cloud-based AI services. - Your project does not involve running models locally but strictly relies on public cloud resources for inference server operations. - You do not have the technical capability to run an OpenAI-compatible inference server or manage local infrastructure effectively.
- Is airllm or private-gpt more popular on GitHub?
- private-gpt has more GitHub stars (57,329 vs 22,399). Stars measure visibility, not whether either tool fits your constraints.
- Are airllm and private-gpt open source?
- Yes - both are open-source projects on GitHub (airllm: Apache-2.0, private-gpt: Apache-2.0).
- Where can I find alternatives to airllm or private-gpt?
- GraphCanon lists graph-backed alternatives at airllm alternatives and private-gpt alternatives (airllm markdown twin, private-gpt 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 private-gpt?
- airllm: Very active. private-gpt: 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 airllm and private-gpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: airllm trust report; private-gpt trust report.