Home/Compare/ColossalAI vs pai

Comparison

ColossalAI vs pai

Verdict

Pick ColossalAI when colossalAI is primarily Python; pai is JavaScript; pick pai when pai is primarily JavaScript; ColossalAI is Python.

Markdown twin · ColossalAI alternatives · pai alternatives

GraphCanon updated today

ColossalAI logo

ColossalAI

hpcaitech/ColossalAI

41kpushed May 25, 2026
vs
pai logo

pai

microsoft/pai

2.7kpushed Jun 6, 2024

Trust & integrity

SignalColossalAIpai
Maintenance
Steady (46d since push)
As of today · github_public_v1
Archived (765d 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
pai
Resource scheduling and cluster management for AI

Stars

ColossalAI
41k
pai
2.7k

Forks

ColossalAI
4.5k
pai
549

Open issues

ColossalAI
501
pai
282

Language

ColossalAI
Python
pai
JavaScript

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.
pai
-

Persona

ColossalAI
-
pai
-

Runtime

ColossalAI
-
pai
-

License

ColossalAI
Apache-2.0
pai
MIT

Last pushed

ColossalAI
May 25, 2026
pai
Jun 6, 2024

Categories

ColossalAI
Model Training, Inference & Serving
pai
Model Training

Trust and health

Maintenance

ColossalAI
Steady (60%)
pai
Archived (8%)

Days since push

ColossalAI
46d
pai
765d

Archived on GitHub

ColossalAI
No
pai
Yes

Open issues (now)

ColossalAI
501
pai
282

Full report

ColossalAI
Trust report

Choose ColossalAI if…

  • ColossalAI is primarily Python; pai is JavaScript.
  • License: ColossalAI is Apache-2.0, pai is MIT.
  • Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models.
  • Also covers Inference & Serving.
  • 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 pai if…

  • pai is primarily JavaScript; ColossalAI is Python.
  • License: pai is MIT, ColossalAI is Apache-2.0.
  • Tags unique to pai: gpu, cluster-manager, artificial-intelligence, chainer.

When NOT to use pai

  • pai is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency.
  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

Explore

Sources

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

GitHub stars on cards: ColossalAI 41k · pai 2.7k (synced Jul 11, 2026).

Common questions

What is the difference between ColossalAI and pai?
ColossalAI: Making large AI models cheaper, faster and more accessible. pai: Resource scheduling and cluster management for AI. See the comparison table for live GitHub stats and shared categories.
When should I choose ColossalAI over pai?
Choose ColossalAI over pai when ColossalAI is primarily Python; pai is JavaScript; License: ColossalAI is Apache-2.0, pai is MIT; Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
When should I choose pai over ColossalAI?
Choose pai over ColossalAI when pai is primarily JavaScript; ColossalAI is Python; License: pai is MIT, ColossalAI is Apache-2.0; Tags unique to pai: gpu, cluster-manager, artificial-intelligence, chainer.
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 pai?
pai is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is ColossalAI or pai more popular on GitHub?
ColossalAI has more GitHub stars (41,408 vs 2,683). Stars measure visibility, not whether either tool fits your constraints.
Are ColossalAI and pai open source?
Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, pai: MIT).
Where can I find alternatives to ColossalAI or pai?
GraphCanon lists graph-backed alternatives at ColossalAI alternatives and pai alternatives (ColossalAI markdown twin, pai 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 pai?
ColossalAI: Steady. pai: Archived. 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 pai?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ColossalAI trust report; pai trust report.