Comparison
DeepSpeed vs gorilla
Verdict
Pick DeepSpeed if decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
Markdown twin · DeepSpeed alternatives · gorilla alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | DeepSpeed | gorilla |
|---|---|---|
| Maintenance | Very active (0d 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
- DeepSpeed
- Deep learning optimization library for efficient distributed training and inference
- gorilla
- Training and Evaluating LLMs for Function Calls (Tool Calls)
Stars
- DeepSpeed
- 43k
- gorilla
- 13k
Forks
- DeepSpeed
- 4.9k
- gorilla
- 1.4k
Open issues
- DeepSpeed
- 1.3k
- gorilla
- 264
Language
- DeepSpeed
- Python
- gorilla
- Python
Adopt for
- DeepSpeed
- Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
- gorilla
- Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages.
Persona
- DeepSpeed
- -
- gorilla
- -
Runtime
- DeepSpeed
- -
- gorilla
- -
License
- DeepSpeed
- Apache-2.0
- gorilla
- Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes.
Last pushed
- DeepSpeed
- Jul 11, 2026
- gorilla
- Apr 13, 2026
Categories
- DeepSpeed
- Inference & Serving, Model Training
- gorilla
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- DeepSpeed
- Very active (96%)
- gorilla
- Steady (60%)
Days since push
- DeepSpeed
- 0d
- gorilla
- 89d
Open issues (now)
- DeepSpeed
- 1.3k
- gorilla
- 264
Owner type
- DeepSpeed
- Organization
- gorilla
- User
Full report
- DeepSpeed
- Trust report
- gorilla
- Trust report
Choose DeepSpeed if…
- Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning.
- Also covers Inference & Serving.
- - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)
When NOT to use DeepSpeed
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
- - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
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 (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- Last push (deepspeedai/DeepSpeed) · 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 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: DeepSpeed 43k · gorilla 13k (synced Jul 11, 2026).
Common questions
- What is the difference between DeepSpeed and gorilla?
- DeepSpeed: Deep learning optimization library for efficient distributed training and inference. 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 DeepSpeed over gorilla?
- Choose DeepSpeed over gorilla when Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; Also covers Inference & Serving; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters).
- When should I choose gorilla over DeepSpeed?
- Choose gorilla over DeepSpeed 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 DeepSpeed?
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
- 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 DeepSpeed or gorilla more popular on GitHub?
- DeepSpeed has more GitHub stars (42,685 vs 12,940). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSpeed and gorilla open source?
- Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, gorilla: Apache-2.0).
- Where can I find alternatives to DeepSpeed or gorilla?
- GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and gorilla alternatives (DeepSpeed 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, DeepSpeed or gorilla?
- DeepSpeed: Very active. 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 DeepSpeed and gorilla?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; gorilla trust report.