---
title: "LlamaFactory vs infinity"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/hiyouga-llamafactory-vs-michaelfeil-infinity"
tools: ["hiyouga-llamafactory", "michaelfeil-infinity"]
---

# LlamaFactory vs infinity

*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 infinity if infinity is a high-throughput, low-latency serving engine that supports text-embeddings, reranking models, CLIP, CLAP, and ColPaLi, with GPU acceleration including ROCm and TensorRT.

[LlamaFactory](https://llamafactory.readthedocs.io) reports 73k GitHub stars, 8.9k forks, and 1.1k open issues, last pushed Jul 10, 2026. [infinity](https://michaelfeil.github.io/infinity/) has 2.9k stars, 196 forks, and 130 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [LlamaFactory's repository](https://github.com/hiyouga/LlamaFactory) and [infinity's repository](https://github.com/michaelfeil/infinity).

| | [LlamaFactory](/tools/hiyouga-llamafactory.md) | [infinity](/tools/michaelfeil-infinity.md) |
| --- | --- | --- |
| Tagline | Unified Efficient Fine-Tuning of 100+ LLMs & VLMs | High-throughput, low-latency serving engine for text-embeddings and various models |
| Stars | 73,157 | 2,874 |
| Forks | 8,937 | 196 |
| Open issues | 1,067 | 130 |
| 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. | Infinity is a high-throughput, low-latency serving engine that supports text-embeddings, reranking models, CLIP, CLAP, and ColPaLi, with GPU acceleration including ROCm and TensorRT. |
| 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) | [infinity](/tools/michaelfeil-infinity.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 0d | 109d |
| Open issues (now) | 1.1k | 130 |
| Full report | [trust report](/tools/hiyouga-llamafactory/trust.md) | [trust report](/tools/michaelfeil-infinity/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: infinity

- **Adopt for:** Infinity is a high-throughput, low-latency serving engine that supports text-embeddings, reranking models, CLIP, CLAP, and ColPaLi, with GPU acceleration including ROCm and TensorRT.

## Choose when

### Choose LlamaFactory if…

- License: LlamaFactory is Apache-2.0, infinity 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 infinity if…

- License: infinity is MIT, LlamaFactory is Apache-2.0.
- Tags unique to infinity: clap, clip, colpali, docker-container.
- Also covers Inference & Serving.
- When you need to serve embeddings and various models with high throughput and low latency.

## 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 infinity

- Avoid using Infinity if your setup does not require GPU acceleration since its specialized Docker images may introduce unnecessary complexity.
- Do not use Infinity if you are working with models that are not supported by it (such as specific NLP models outside of embeddings and reranking).

## Common questions

### What is the difference between LlamaFactory and infinity?

LlamaFactory: Unified Efficient Fine-Tuning of 100+ LLMs & VLMs. infinity: High-throughput, low-latency serving engine for text-embeddings and various models. See the comparison table for live GitHub stats and shared categories.

### When should I choose LlamaFactory over infinity?

Choose LlamaFactory over infinity when License: LlamaFactory is Apache-2.0, infinity 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 infinity over LlamaFactory?

Choose infinity over LlamaFactory when License: infinity is MIT, LlamaFactory is Apache-2.0; Tags unique to infinity: clap, clip, colpali, docker-container; Also covers Inference & Serving; When you need to serve embeddings and various models with high throughput and low latency.

### 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 infinity?

Avoid using Infinity if your setup does not require GPU acceleration since its specialized Docker images may introduce unnecessary complexity. Do not use Infinity if you are working with models that are not supported by it (such as specific NLP models outside of embeddings and reranking).

### Is LlamaFactory or infinity more popular on GitHub?

LlamaFactory has more GitHub stars (73,157 vs 2,874). Stars measure visibility, not whether either tool fits your constraints.

### Are LlamaFactory and infinity open source?

Yes - both are open-source projects on GitHub (LlamaFactory: Apache-2.0, infinity: MIT).

### Where can I find alternatives to LlamaFactory or infinity?

GraphCanon lists graph-backed alternatives at [LlamaFactory alternatives](/tools/hiyouga-llamafactory/alternatives) and [infinity alternatives](/tools/michaelfeil-infinity/alternatives) ([LlamaFactory markdown twin](/tools/hiyouga-llamafactory/alternatives.md), [infinity markdown twin](/tools/michaelfeil-infinity/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-michaelfeil-infinity.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, LlamaFactory or infinity?

LlamaFactory: Very active. infinity: 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 infinity?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LlamaFactory trust report](/tools/hiyouga-llamafactory/trust); [infinity trust report](/tools/michaelfeil-infinity/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/_