# FAQ
### 1. **Which model should I choose for fine-tuning?**
* Small models (<=1B parameters) --> for testing or light workloads
* Medium models (7B-13B) --> balanced performance and cost
* Large models (30B+) --> for complex tasks, usually requires multi-node setup
* Instruction-tuned models are preferred if your task is prompt-response based
### **2. How long does fine-tuning take?**
It depends on:
* Model size (a few hours for small models, several days for very large ones)
* Dataset size
* Your hardware setup (hyperparameters & infrastructure)
Typically, it ranges from a few hours to several days.
### **3. What do your need to prepare before fine-tuning a model?**
You'll need:
* Strictly follow the expected dataset structure for the model you're fine-tuning. More details about Data sample, visit here:
* Clean, diverse, and non-duplicated data.
* A clear objective for fine-tuning (e.g., tech support, customer service, content writing, etc.).
### **4. How many GPUs do you need to fine-tune a model?**
It depends on the model size:
* <1B parameters: 1 GPU (24 GB VRAM) is sufficient
* 7B models: 2-4 GPUs (40 GB VRAM each)
* 13B models: 4-8 GPUs recommended
* 30B+ models: Requires 8+ GPUs and multi-node setup
### **5. Do I need multiple nodes or just one node?**
* For small to medium models (up to 13B), a single node with multiple GPUs is enough.
* For larger models (30B+), multi-node setups are recommended for better memory and performance.
### **6. What is the minimum GPU memory required?**
* At least **24GB VRAM per GPU** for standard fine-tuning
* Without **LoRA/QLoRA methods**, you can fine-tune on GPUs with 8-16GB VRAM
### **7. Does the size of my training dataset affect hardware needs?**
Yes. Larger datasets require more VRAM, RAM, and storage.
* Datasets < 20GB --> can use **Managed volume**
* Datasets > 20GB --> require **Dedicated network volume**