> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-de/training/training.md).

# Überblick

Trainiere und feintune KI-Modelle auf CLORE.AI-GPUs.

## Verfügbare Anleitungen

| Leitfaden                                                                   | Anwendungsfall                                 | Schwierigkeitsgrad |
| --------------------------------------------------------------------------- | ---------------------------------------------- | ------------------ |
| [Jupyter-ML-Training](/guides/guides_v2-de/training/jupyter-ml-training.md) | Interaktives Training                          | Einfach            |
| [DreamBooth](/guides/guides_v2-de/training/dreambooth.md)                   | Benutzerdefinierte SD-Motive                   | Mittel             |
| [Kohya-Training](/guides/guides_v2-de/training/kohya-training.md)           | LoRA/LyCORIS-Training                          | Mittel             |
| [LLM feinabstimmen](/guides/guides_v2-de/training/finetune-llm.md)          | LLM-Feinabstimmung                             | Fortgeschritten    |
| [DeepSpeed](/guides/guides_v2-de/training/deepspeed-training.md)            | Verteiltes Training                            | Fortgeschritten    |
| [HuggingFace](/guides/guides_v2-de/training/huggingface-transformers.md)    | Transformers-Training                          | Mittel             |
| [Unsloth](/guides/guides_v2-de/training/unsloth-finetune.md)                | Schnelle LLM-Feinabstimmung                    | Mittel             |
| [Axolotl](/guides/guides_v2-de/training/axolotl-training.md)                | YAML-zentrierte Feinabstimmung                 | Mittel             |
| [LLaMA-Factory](/guides/guides_v2-de/training/llama-factory.md)             | Einfache LLM-Trainingsoberfläche               | Einfach            |
| [TRL](/guides/guides_v2-de/training/trl.md)                                 | Transformer-Verstärkungslernen                 | Fortgeschritten    |
| [LitGPT](/guides/guides_v2-de/training/litgpt.md)                           | Lightning-basiertes Training                   | Mittel             |
| [Mergekit](/guides/guides_v2-de/training/mergekit.md)                       | Werkzeugkasten zum Zusammenführen von Modellen | Einfach            |

## GPU-Empfehlungen

{% hint style="warning" %}
**Multi-GPU-Rigs der 80GB-Klasse sind auf dem Clore.ai-Marktplatz nicht gelistet.** Die größten heute gelisteten Systeme sind 4× RTX PRO 6000 Blackwell (je 96 GB, 380 GB gesamt) und 8–11× RTX 5090 (je 32 GB). Kapazitäten für A100 / H200 / B200 werden als [Bare Metal](https://clore.ai/bare-metal) auf Anfrage verkauft. Prüfe [GPU-Preise & Verfügbarkeit](/guides/guides_v2-de/erste-schritte/pricing.md) bevor du eine Bereitstellung dimensionierst.
{% endhint %}

| Aufgabe                  | Minimum  | Empfohlen |
| ------------------------ | -------- | --------- |
| LoRA (SD)                | RTX 3060 | RTX 3090  |
| DreamBooth               | RTX 3090 | RTX 4090  |
| LLM-Feinabstimmung (7B)  | RTX 3090 | A100 40GB |
| LLM-Feinabstimmung (70B) | 4x A100  | 8x A100   |

## Trainingstypen

### Bildmodelle

* **LoRA** - Leichter Adapter, schnelles Training
* **DreamBooth** - Vollständige Feinabstimmung für Konzepte
* **Textuelle Inversion** - Neue Tokens lernen

### Sprachmodelle

* **LoRA/QLoRA** - Speicher-effiziente Feinabstimmung
* **Vollständige Feinabstimmung** - Beste Qualität, benötigt mehr VRAM

## Tipps

* Verwende **Spot** Bestellungen für lange Trainingsläufe
* Aktiviere Gradient Checkpointing, um VRAM zu sparen
* Überwache das Training mit TensorBoard
* Checkpoints häufig speichern

## Verwandte Leitfäden

* [Sprachmodelle](/guides/guides_v2-de/sprachmodelle/language-models.md)
* [Bilderzeugung](/guides/guides_v2-de/bildgenerierung/image-generation.md)


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