--- title: "LlamaFactory vs quant.cpp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-quantumaikr-quant-cpp" tools: ["hiyouga-llamafactory", "quantumaikr-quant-cpp"] --- # LlamaFactory vs quant.cpp *GraphCanon updated Jul 11, 2026* ## Verdict Pick LlamaFactory when llamaFactory is primarily Python; quant.cpp is C; pick quant.cpp when quant.cpp is primarily C; LlamaFactory is Python. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [quant.cpp](https://github.com/quantumaikr/quant.cpp) has 395 stars, 43 forks, and 11 open issues, last pushed Apr 26, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [quant.cpp's repository](https://github.com/quantumaikr/quant.cpp). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [quant.cpp](/tools/quantumaikr-quant-cpp.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | LLM inference with 7x longer context. Pure C, zero dependencies. Lossless KV cache compression + single-header library. | | Stars | 73,157 | 395 | | Forks | 8,937 | 43 | | Open issues | 1,067 | 11 | | Language | Python | C | | Adopt for | LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [quant.cpp](/tools/quantumaikr-quant-cpp.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 76d | | Open issues (now) | 1.1k | 11 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/quantumaikr-quant-cpp/trust.md) | ## Decision facts: LlamaFactory - **Adopt for:** LlamaFactory is a sophisticated tool for fine-tuning numerous large language models and visual language models efficiently using various methods such as LoRA, QLoRA, RLHF, and quantization. ## Choose when ### Choose LlamaFactory if… - LlamaFactory is primarily Python; quant.cpp is C. - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose quant.cpp if… - quant.cpp is primarily C; LlamaFactory is Python. - Tags unique to quant.cpp: delta-compression, embeddable, gguf, kv-cache. - Also covers Inference & Serving. ## When NOT to use LlamaFactory - When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. - If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ## When NOT to use quant.cpp - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - 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 LlamaFactory and quant.cpp? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. quant.cpp: LLM inference with 7x longer context. Pure C, zero dependencies. Lossless KV cache compression + single-header library.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over quant.cpp? Choose LlamaFactory over quant.cpp when LlamaFactory is primarily Python; quant.cpp is C; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose quant.cpp over LlamaFactory? Choose quant.cpp over LlamaFactory when quant.cpp is primarily C; LlamaFactory is Python; Tags unique to quant.cpp: delta-compression, embeddable, gguf, kv-cache; Also covers Inference & Serving. ### When should I avoid LlamaFactory? When you are looking to fine-tune less popular or niche models that are not supported within the 100+ models covered by LlamaFactory. If your project specifically requires custom fine-tuning methods not available in this repository, such as certain versions of PEFT (Parameter Efficient Fine-Tuning) techniques excluding LoRA and QLoa ### When should I avoid quant.cpp? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is LlamaFactory or quant.cpp more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 395). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and quant.cpp open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, quant.cpp: Apache-2.0). ### Where can I find alternatives to LlamaFactory or quant.cpp? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [quant.cpp alternatives](/tools/quantumaikr-quant-cpp/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [quant.cpp markdown twin](/tools/quantumaikr-quant-cpp/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/hiyouga-llamafactory-vs-quantumaikr-quant-cpp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or quant.cpp? LlamaFactory: Very active. quant.cpp: 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 LlamaFactory and quant.cpp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [quant.cpp trust report](/tools/quantumaikr-quant-cpp/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hiyouga-llamafactory`](/api/graphcanon/graph?tool=hiyouga-llamafactory) - 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/_