--- title: "LlamaFactory vs MInference" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-microsoft-minference" tools: ["hiyouga-llamafactory", "microsoft-minference"] --- # LlamaFactory vs MInference *GraphCanon updated Jul 12, 2026* ## Verdict Pick LlamaFactory if 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; pick MInference if mInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy. [LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [MInference](https://aka.ms/MInference) has 1.2k stars, 78 forks, and 93 open issues, last pushed Apr 8, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [MInference's repository](https://github.com/microsoft/MInference). | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [MInference](/tools/microsoft-minference.md) | | --- | --- | --- | | Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | Accelerates Long-context LLMs' inference through approximate sparse calculation for attention. | | Stars | 73,157 | 1,221 | | Forks | 8,937 | 78 | | Open issues | 1,067 | 93 | | Language | Python | 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. | MInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [MInference](/tools/microsoft-minference.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 94d | | Open issues (now) | 1.1k | 93 | | Owner type | User | Organization | | Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/microsoft-minference/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. ## Decision facts: MInference - **Requirements:** Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration. - **Adopt for:** MInference accelerates long-context LLMs' inference by up to 10x via approximate sparse calculation techniques while preserving model accuracy. ## Choose when ### Choose LlamaFactory if… - License: LlamaFactory is Apache-2.0, MInference is MIT. - Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning. - Also covers LLM Frameworks, Model Training. - When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### Choose MInference if… - License: MInference is MIT, LlamaFactory is Apache-2.0. - Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration.. - Tags unique to MInference: attention mechanism, flashattention-2, inference acceleration, long-context llms. - Also covers Inference & Serving. - MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs. ## 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 MInference - Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation. - MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental. ## Common questions ### What is the difference between LlamaFactory and MInference? LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. MInference: Accelerates Long-context LLMs' inference through approximate sparse calculation for attention.. See the comparison table for live GitHub stats and shared categories. ### When should I choose LlamaFactory over MInference? Choose LlamaFactory over MInference when License: LlamaFactory is Apache-2.0, MInference is MIT; Tags unique to LlamaFactory: agent, ai, deepseek, fine-tuning; Also covers LLM Frameworks, Model Training; When you need to fine-tune over 100 different LLMs or VLMs with efficient methods like LoRA or QLoRA. ### When should I choose MInference over LlamaFactory? Choose MInference over LlamaFactory when License: MInference is MIT, LlamaFactory is Apache-2.0; Requirements: Min 8 GB RAM; MInference requires at least Torch and optionally FlashAttention-2 for maximum efficiency.; Triton for faster deployment and integration.; Tags unique to MInference: attention mechanism, flashattention-2, inference acceleration, long-context llms; Also covers Inference & Serving; MInference is ideal for scenarios where significant reduction in inference latency is needed without sacrificing the accuracy of long-context LLM outputs. ### 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 MInference? Avoid using MInference if your application does not benefit from or cannot tolerate slight variations in inference times due to its use of approximate sparse calculation. MInference might not be suitable for applications where the model's accuracy is critical and any reduction in the precision introduced by approximations would be detrimental. ### Is LlamaFactory or MInference more popular on GitHub? LlamaFactory has more GitHub stars (73,157 vs 1,221). Stars measure visibility, not whether either tool fits your constraints. ### Are LlamaFactory and MInference open source? Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, MInference: MIT). ### Where can I find alternatives to LlamaFactory or MInference? GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [MInference alternatives](/tools/microsoft-minference/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [MInference markdown twin](/tools/microsoft-minference/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-microsoft-minference.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LlamaFactory or MInference? LlamaFactory: Very active. MInference: Slowing. 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 MInference? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [MInference trust report](/tools/microsoft-minference/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/_