Comparison
ColossalAI vs gorilla
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 gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
Markdown twin · ColossalAI alternatives · gorilla alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | ColossalAI | gorilla |
|---|---|---|
| Maintenance | Steady (46d since push) As of today · github_public_v1 | Steady (89d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal 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
- gorilla
- Training and Evaluating LLMs for Function Calls (Tool Calls)
Stars
- ColossalAI
- 41k
- gorilla
- 13k
Forks
- ColossalAI
- 4.5k
- gorilla
- 1.4k
Open issues
- ColossalAI
- 501
- gorilla
- 264
Language
- ColossalAI
- Python
- gorilla
- 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.
- gorilla
- Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
Persona
- ColossalAI
- -
- gorilla
- -
Runtime
- ColossalAI
- -
- gorilla
- -
License
- ColossalAI
- Apache-2.0
- gorilla
- Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes.
Last pushed
- ColossalAI
- May 25, 2026
- gorilla
- Apr 13, 2026
Categories
- ColossalAI
- Inference & Serving, Model Training
- gorilla
- Evaluation & Observability, Model Training
Trust and health
Days since push
- ColossalAI
- 46d
- gorilla
- 89d
Open issues (now)
- ColossalAI
- 501
- gorilla
- 264
Owner type
- ColossalAI
- Organization
- gorilla
- User
Full report
- ColossalAI
- Trust report
- gorilla
- Trust report
Shared compatibility
- Python · ColossalAI: Python runtime · gorilla: Python runtime
Choose ColossalAI if…
- Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning.
- 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 gorilla if…
- Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning..
- Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api.
- Also covers Evaluation & Observability.
- You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively.
When NOT to use gorilla
- Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs.
- If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard.
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 (ShishirPatil/gorilla) · observed Jul 11, 2026
- GitHub forks (ShishirPatil/gorilla) · observed Jul 11, 2026
- Last push (ShishirPatil/gorilla) · observed Apr 13, 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 · gorilla 13k (synced Jul 11, 2026).
Common questions
- What is the difference between ColossalAI and gorilla?
- ColossalAI: Making large AI models cheaper, faster and more accessible. gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). See the comparison table for live GitHub stats and shared categories.
- When should I choose ColossalAI over gorilla?
- Choose ColossalAI over gorilla when Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- When should I choose gorilla over ColossalAI?
- Choose gorilla over ColossalAI when Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.; Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api; Also covers Evaluation & Observability; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively.
- 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 gorilla?
- Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard.
- Is ColossalAI or gorilla more popular on GitHub?
- ColossalAI has more GitHub stars (41,408 vs 12,940). Stars measure visibility, not whether either tool fits your constraints.
- Are ColossalAI and gorilla open source?
- Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, gorilla: Apache-2.0).
- Where can I find alternatives to ColossalAI or gorilla?
- GraphCanon lists graph-backed alternatives at ColossalAI alternatives and gorilla alternatives (ColossalAI markdown twin, gorilla 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 gorilla?
- ColossalAI: Steady. gorilla: Steady. 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 gorilla?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ColossalAI trust report; gorilla trust report.