--- title: "krasis vs LlamaFactory" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/brontoguana-krasis-vs-hiyouga-llamafactory" tools: ["brontoguana-krasis", "hiyouga-llamafactory"] --- # krasis vs LlamaFactory *GraphCanon updated Jul 11, 2026* ## Verdict Pick krasis when krasis is primarily C++; LlamaFactory is Python; pick LlamaFactory when llamaFactory is primarily Python; krasis is C++. [krasis](https://github.com/brontoguana/krasis) reports 480 GitHub stars, 27 forks, and 8 open issues, last pushed Jul 9, 2026. [LlamaFactory](https://llamafactory.readthedocs.io) has 73k stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [krasis's repository](https://github.com/brontoguana/krasis) and [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory). | | [krasis](/tools/brontoguana-krasis.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Tagline | Krasis is a Hybrid LLM runtime which focuses on efficient running of larger models on consumer grade VRAM limited hardware | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | | Stars | 480 | 73,157 | | Forks | 27 | 8,937 | | Open issues | 8 | 1,067 | | Language | C++ | Python | | 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 | Other | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [krasis](/tools/brontoguana-krasis.md) | [LlamaFactory](/tools/hiyouga-llamafactory.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 8 | 1.1k | | Full report | [trust report](/tools/brontoguana-krasis/trust.md) | [trust report](/tools/hiyouga-llamafactory/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 krasis if… - krasis is primarily C++; LlamaFactory is Python. - License: krasis is Other, LlamaFactory is Apache-2.0. - Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference. - Also covers Inference & Serving. ### Choose LlamaFactory if… - LlamaFactory is primarily Python; krasis is C++. - License: LlamaFactory is Apache-2.0, krasis is Other. - 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 NOT to use krasis - 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. ## 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 ## Common questions ### What is the difference between krasis and LlamaFactory? krasis: Krasis is a Hybrid LLM runtime which focuses on efficient running of larger models on consumer grade VRAM limited hardware. LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose krasis over LlamaFactory? Choose krasis over LlamaFactory when krasis is primarily C++; LlamaFactory is Python; License: krasis is Other, LlamaFactory is Apache-2.0; Tags unique to krasis: cpu-inference, gguf-model-support, gpu-inference, high-performance-inference; Also covers Inference & Serving. ### When should I choose LlamaFactory over krasis? Choose LlamaFactory over krasis when LlamaFactory is primarily Python; krasis is C++; License: LlamaFactory is Apache-2.0, krasis is Other; 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 avoid krasis? 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. ### 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 ### Is krasis or LlamaFactory more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 480). Stars measure visibility, not whether either tool fits your constraints. ### Are krasis and LlamaFactory open source? Yes - both are open-source projects on GitHub (krasis: Other, LlamaFactory: Apache-2.0). ### Where can I find alternatives to krasis or LlamaFactory? GraphCanon lists graph-backed alternatives at [krasis alternatives](/tools/brontoguana-krasis/alternatives) and [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) ([krasis markdown twin](/tools/brontoguana-krasis/alternatives.md), [LlamaFactory markdown twin](/tools/hiyouga-llamafactory/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/brontoguana-krasis-vs-hiyouga-llamafactory.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, krasis or LlamaFactory? krasis: Very active. LlamaFactory: Very active. 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 krasis and LlamaFactory? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [krasis trust report](/tools/brontoguana-krasis/trust); [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=brontoguana-krasis`](/api/graphcanon/graph?tool=brontoguana-krasis) - 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/_