--- title: "DeepSpeed vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-shishirpatil-gorilla" tools: ["deepspeedai-deepspeed", "shishirpatil-gorilla"] --- # DeepSpeed vs gorilla *GraphCanon updated Jul 11, 2026* ## 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. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 11, 2026. [gorilla](https://gorilla.cs.berkeley.edu/) has 13k stars, 1.4k forks, and 264 open issues, last pushed Apr 13, 2026. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 42,685 | 12,940 | | Forks | 4,883 | 1,387 | | Open issues | 1,302 | 264 | | Language | Python | Python | | Adopt for | 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 specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Inference & Serving, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 89d | | Open issues (now) | 1.3k | 264 | | Owner type | Organization | User | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** 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. ## Decision facts: gorilla - **Pricing:** freemium - **Requirements:** Gorilla works best with Python environments and requires installation through pip or local repository cloning. - **Adopt for:** Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. - **License detail:** Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. ## Choose when ### 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) ### 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 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 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. ## 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](/tools/deepspeedai-deepspeed/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [gorilla markdown twin](/tools/shishirpatil-gorilla/alternatives.md)), 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](/compare/deepspeedai-deepspeed-vs-shishirpatil-gorilla.md) 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](/tools/deepspeedai-deepspeed/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepspeedai-deepspeed`](/api/graphcanon/graph?tool=deepspeedai-deepspeed) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_