--- title: "caffe vs alpaca-lora" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bvlc-caffe-vs-tloen-alpaca-lora" tools: ["bvlc-caffe", "tloen-alpaca-lora"] --- # caffe vs alpaca-lora *GraphCanon updated Jul 11, 2026* ## Verdict Pick caffe when caffe is primarily C++; alpaca-lora is Jupyter Notebook; pick alpaca-lora when alpaca-lora is primarily Jupyter Notebook; caffe is C++. [caffe](http://caffe.berkeleyvision.org/) reports 35k GitHub stars, 18k forks, and 1.2k open issues, last pushed Jul 31, 2024. [alpaca-lora](https://github.com/tloen/alpaca-lora) has 19k stars, 2.2k forks, and 366 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [caffe's repository](https://github.com/BVLC/caffe) and [alpaca-lora's repository](https://github.com/tloen/alpaca-lora). | | [caffe](/tools/bvlc-caffe.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Tagline | Caffe: a fast open framework for deep learning. | Instruct-tune LLaMA on consumer hardware | | Stars | 34,574 | 18,913 | | Forks | 18,458 | 2,185 | | Open issues | 1,209 | 366 | | Language | C++ | Jupyter Notebook | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | Computer Vision, Vector Databases | Computer Vision, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caffe](/tools/bvlc-caffe.md) | [alpaca-lora](/tools/tloen-alpaca-lora.md) | | --- | --- | --- | | Days since push | 710d | 712d | | Open issues (now) | 1.2k | 366 | | Owner type | Organization | User | | Security scan | No lockfile | 1 critical, 5 high, 12 medium, 28 low (1 critical, 5 high, 12 medium, 28 low) | | Full report | [trust report](/tools/bvlc-caffe/trust.md) | [trust report](/tools/tloen-alpaca-lora/trust.md) | ## Choose when ### Choose caffe if… - caffe is primarily C++; alpaca-lora is Jupyter Notebook. - License: caffe is Other, alpaca-lora is Apache-2.0. - Tags unique to caffe: c++, deep-learning, machine-learning, vision. - Also covers Vector Databases. ### Choose alpaca-lora if… - alpaca-lora is primarily Jupyter Notebook; caffe is C++. - License: alpaca-lora is Apache-2.0, caffe is Other. - Tags unique to alpaca-lora: jupyter notebook. - Also covers Inference & Serving, Model Training. - alpaca-lora ships Docker support for self-hosted deployment. ## When NOT to use caffe - Last GitHub push was 710 days ago (dormant maintenance, Jul 31, 2024). Validate activity before betting a new project on caffe. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## When NOT to use alpaca-lora - Last GitHub push was 712 days ago (dormant maintenance, Jul 29, 2024). Validate activity before betting a new project on alpaca-lora. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between caffe and alpaca-lora? caffe: Caffe: a fast open framework for deep learning.. alpaca-lora: Instruct-tune LLaMA on consumer hardware. See the comparison table for live GitHub stats and shared categories. ### When should I choose caffe over alpaca-lora? Choose caffe over alpaca-lora when caffe is primarily C++; alpaca-lora is Jupyter Notebook; License: caffe is Other, alpaca-lora is Apache-2.0; Tags unique to caffe: c++, deep-learning, machine-learning, vision; Also covers Vector Databases. ### When should I choose alpaca-lora over caffe? Choose alpaca-lora over caffe when alpaca-lora is primarily Jupyter Notebook; caffe is C++; License: alpaca-lora is Apache-2.0, caffe is Other; Tags unique to alpaca-lora: jupyter notebook; Also covers Inference & Serving, Model Training; alpaca-lora ships Docker support for self-hosted deployment. ### When should I avoid caffe? Last GitHub push was 710 days ago (dormant maintenance, Jul 31, 2024). Validate activity before betting a new project on caffe. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### When should I avoid alpaca-lora? Last GitHub push was 712 days ago (dormant maintenance, Jul 29, 2024). Validate activity before betting a new project on alpaca-lora. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is caffe or alpaca-lora more popular on GitHub? caffe has more GitHub stars (34,574 vs 18,913). Stars measure visibility, not whether either tool fits your constraints. ### Are caffe and alpaca-lora open source? Yes - both are open-source projects on GitHub (caffe: Other, alpaca-lora: Apache-2.0). ### Where can I find alternatives to caffe or alpaca-lora? GraphCanon lists graph-backed alternatives at [caffe alternatives](/tools/bvlc-caffe/alternatives) and [alpaca-lora alternatives](/tools/tloen-alpaca-lora/alternatives) ([caffe markdown twin](/tools/bvlc-caffe/alternatives.md), [alpaca-lora markdown twin](/tools/tloen-alpaca-lora/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/bvlc-caffe-vs-tloen-alpaca-lora.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caffe or alpaca-lora? caffe: Dormant. alpaca-lora: Dormant. 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 caffe and alpaca-lora? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caffe trust report](/tools/bvlc-caffe/trust); [alpaca-lora trust report](/tools/tloen-alpaca-lora/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bvlc-caffe`](/api/graphcanon/graph?tool=bvlc-caffe) - 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/_