Comparison
ColossalAI vs private-gpt
Verdict
Pick ColossalAI if colossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models; 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 · ColossalAI alternatives · private-gpt alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | ColossalAI | private-gpt |
|---|---|---|
| Maintenance | Steady (46d since push) As of today · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- ColossalAI
- Making large AI models cheaper, faster and more accessible
- private-gpt
- Complete API layer for private AI applications on local models
Stars
- ColossalAI
- 41k
- private-gpt
- 57k
Forks
- ColossalAI
- 4.5k
- private-gpt
- 7.6k
Open issues
- ColossalAI
- 501
- private-gpt
- 5
Language
- ColossalAI
- Python
- private-gpt
- Python
Adopt for
- ColossalAI
- ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
- 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
- ColossalAI
- -
- private-gpt
- -
Runtime
- ColossalAI
- -
- private-gpt
- -
License
- ColossalAI
- Apache-2.0
- private-gpt
- Apache-2.0
Last pushed
- ColossalAI
- May 25, 2026
- private-gpt
- Jul 10, 2026
Categories
- ColossalAI
- Model Training, Inference & Serving
- private-gpt
- Inference & Serving
Trust and health
Maintenance
- ColossalAI
- Steady (60%)
- private-gpt
- Very active (96%)
Days since push
- ColossalAI
- 46d
- private-gpt
- 0d
Open issues (now)
- ColossalAI
- 501
- private-gpt
- 5
Full report
- ColossalAI
- Trust report
- private-gpt
- Trust report
Shared compatibility
- Python · ColossalAI: Python runtime · private-gpt: Python runtime
Choose ColossalAI if…
- Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models.
- Also covers Model Training.
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
When NOT to use ColossalAI
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems.
- Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series).
- You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
Choose private-gpt if…
- Requirements: Min 8 GB RAM; Requires Docker.
- Tags unique to private-gpt: text-to-sql, on-premise, tools, rag.
- 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 (hpcaitech/ColossalAI) · observed Jul 11, 2026
- GitHub forks (hpcaitech/ColossalAI) · observed Jul 11, 2026
- Last push (hpcaitech/ColossalAI) · observed May 25, 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 (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: ColossalAI 41k · private-gpt 57k (synced Jul 11, 2026).
Common questions
- What is the difference between ColossalAI and private-gpt?
- ColossalAI: Making large AI models cheaper, faster and more accessible. 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 ColossalAI over private-gpt?
- Choose ColossalAI over private-gpt when Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; Also covers Model Training; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- When should I choose private-gpt over ColossalAI?
- Choose private-gpt over ColossalAI when Requirements: Min 8 GB RAM; Requires Docker; Tags unique to private-gpt: text-to-sql, on-premise, tools, rag; 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 ColossalAI?
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7.
- 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 ColossalAI or private-gpt more popular on GitHub?
- private-gpt has more GitHub stars (57,329 vs 41,408). Stars measure visibility, not whether either tool fits your constraints.
- Are ColossalAI and private-gpt open source?
- Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, private-gpt: Apache-2.0).
- Where can I find alternatives to ColossalAI or private-gpt?
- GraphCanon lists graph-backed alternatives at ColossalAI alternatives and private-gpt alternatives (ColossalAI 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, ColossalAI or private-gpt?
- ColossalAI: Steady. 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 ColossalAI and private-gpt?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ColossalAI trust report; private-gpt trust report.